Research ArticleTowards Reinforced Brain Tumor Segmentation on MRI ImagesBased on Temperature Changes on Pathologic Area

Abdelmajid Bousselham Omar Bouattane Mohamed Youssfi and Abdelhadi Raihani

SSDIA Laboratory ENSET Mohammedia University Hassan 2 Casablanca Morocco

Correspondence should be addressed to Abdelmajid Bousselham abdelmajidbousselhamgmailcom

Received 13 November 2018 Revised 31 January 2019 Accepted 6 February 2019 Published 3 March 2019

Academic Editor Lizhi Sun

Copyright copy 2019 Abdelmajid Bousselham et alThis is an open access article distributed under the Creative Commons AttributionLicensewhichpermits unrestricteduse distribution and reproduction in anymedium provided the original work is properly cited

Brain tumor segmentation is the process of separating the tumor from normal brain tissues in clinical routine it provides usefulinformation for diagnosis and treatment planning However it is still a challenging task due to the irregular form and confusingboundaries of tumors Tumor cells thermally represent a heat source their temperature is high compared to normal brain cellsThe main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce falsepositive and false negative results of segmentation performed in MRI images Pennes bioheat equation was solved numericallyusing the finite difference method to simulate the temperature distribution in the brain Gaussian noises of plusmn2 were added tothe simulated temperatures Canny edge detector was used to detect tumor contours from the calculated thermal map as thecalculated temperature showed a large gradient in tumor contours The proposed method is compared to ChanndashVese based levelset segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truthThe method is tested in four different phantom patients by considering different tumor volumes and locations and 50 syntheticpatients taken from BRATS 2012 and BRATS 2013 The obtained results in all patients showed significant improvement using theproposed method compared to segmentation by level set method with an average of 08 of the tumor area and 248 of healthytissue was differentiated using thermal images only We conclude that tumor contours delineation based on tumor temperaturechanges can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic

1 Introduction

A brain tumor represents a set of abnormal cells thatreproduce in the brain in an uncontrolled way There arelarge varieties of brain tumor types that are classified intotwo categories benign (noncancerous) brain tumors are lessaggressive formed slowly and most often remain isolatedfrom surrounding brain normal tissues they do not spreadto other regions of the brain or other parts in the humanbody and are generally easier to surgically extract thanmalignancies Malignant brain tumors (cancerous) are notalways easy to distinguish them from surrounding normaltissues Therefore it is sometimes difficult to extract thementirely without damaging the surrounding brain tissues(httpbraintumororg) The number of people affected bymalignant brain tumors has been increasing in the last fewdecades According to the American cancer society [1 2] inthe US for 2017 there were an estimated number of 23800

new cases which increased with 30 cases compared to 2016(23770) and 16700 estimated deaths with an increase of 650cases compared to 2016 (16050)

Magnetic Resonance Imaging or MRI is a noninvasivemedical imagingmodality commonly used in the clinical rou-tine as it offers images with high spatial resolution and highcontrast between soft tissues MRI provides rich informationabout shape size and localization of brain tumors for moreaccurate diagnosis and treatment planning [3 4] Thereforemost of the research in medical diagnosis and delineationof brain tumors uses MRI images Various MRI sequencescan be created they are called weighted images such as T1-weighted T2-weighted Proton-DensityWeighted and Fluid-Attenuated Inversion Recovery (FLAIR) T1-weighted imageprovides a better segmentation for brain tissues due to thehigh contrast between gray and whitematter [5] T1-weightedcontrast-enhanced images and FLAIR are widely used forbrain tumors structure diagnostic as it makes tumor region

HindawiInternational Journal of Biomedical ImagingVolume 2019 Article ID 1758948 18 pageshttpsdoiorg10115520191758948

2 International Journal of Biomedical Imaging

hyperintense In this work we have collected synthetic T1-weighted contrast-enhanced and Flair MRI images of allsubjects as experimental data to test our approach

Accurate segmentation of brain tumors fromMRI imagesrepresents a crucial and challenging task in diagnosis andtreatment planning Image segmentation is an active fieldin medical imaging which consists in extracting from theimage one or more regions forming the area of interestVarious algorithms have been developed in the literature toperform brain tumor detection including threshold-basedmethods [6 7] region-based methods [8 9] deformablemethods [10ndash13] classification methods [14 15] and deeplearning [16ndash18] Deformable models are among the mostpopular methods used for brain tumor segmentation in MRIimages They are represented by curves (2D) or surfaces(3D) defined in an image that move by the influence of twoforces internal or local forces defined in the curve to keepit smooth during the deformation process while externalforces are computed from image data in order to move thecurve towards the object boundary sought In the deformablemodels we distinguish two principal categories parametricdeformable models or snakes [19] and geometric deformablemodels The parametric deformable models necessitate aparametric representation during deformation of the curveThese later have difficulty in topology changes to split andmerge contours to segment multiple objects Geometricdeformable models or level sets proposed by Osher andSethian [20] move based on geometric measurements suchas the curve normal and curvature The advantage of thesemodels is their capacity for topological changes during curvepropagation

Brain tumor segmentation consists of extracting thetumor region from healthy brain tissues the existence ofbrain tumors can often be detectable However accurateand effective segmentation of tumors remains a challengingtask since the tumors can have different sizes and loca-tions Their structures are often nonrigid and complex inshape and have various appearance properties Besides theyhave intensities overlapping with normal brain tissues andespecially in tumor borders they show significant variableappearances from patient to patient [21] due to the need toadd physical information of tumor to reinforce algorithmssegmentation for more accurate and effective extraction Inthe present work we investigate the effect of temperature onsegmentation in MRI images Each tissue in human bodyhas a thermal signature in the presence of abnormality liketumors the thermal signature of the tissue changes and themeasurement of temperature changes can be helpful for theestimation of the existence and localization of an internalabnormality [22] in recent years was widely used as a toolfor tumors diagnostic [23ndash26] We have used temperature todelineate tumor contours and compared the obtained resultswith segmentation by the level set method

Human body temperature distribution depends on sev-eral factors including heat energy generated by cellularmetabolism and blood flow as these are altered in dis-ease the temperature distribution changes in pathologicaltissues The blood flow plays an essential role in the bodythermoregulation mechanism which removes heat from a

region with a higher temperature and increases heat in thecooled region Tumor cells generally generate more heat thanadjacent healthy cells due to their highmetabolic activity andthe blood flow In the tumorous regions the blood flow can besignificantly less than that in the surrounding healthy tissues[27]Therefore heat energy generated by the tumormetabolicheat generation is dissipated less rapidly from the tumor thanfrom the surrounding healthy normal tissues

Consequently the tumor temperature rises higher thannormal tissues [28] Kateb et al [29] showed a significantdifference in brain tumor temperature compared to normalbrain tissues up to 33∘C in the tumor center (364∘C)compared to the surrounding normal tissues temperature(331∘C) and demonstrated that it can be used to delineatethe margins of brain tumors Therefore the temperaturedistribution can provide additional information about braintumors Numerous studies used temperature distribution todetect and estimate tumor size and location using thermalimaging (thermography) [25 30ndash33]

In this paper we developed a new approach to improvethe segmentation of brain tumors performance in termof accuracy based on temperature profiles changes in thetumorous region The temperature distribution in the brainwith the tumor is calculated using Pennes bioheat equationNext Canny edge detection method was applied in thecalculated thermal image to estimate tumor contours basedon the abrupt change of temperature in tumor contours Theobtained results are comparedwithChanndashVese based level setsegmentation in MRI images

The rest of this paper presents the proposed method inSection 2 several tests results and discussion in Section 3and finally the conclusion in Section 4

2 Materials and Methods

21 Temperature Calculation Temperature distribution inthe brain with tumors was simulated by Pennes bioheattransfer equation [34] which models heat transfer withinbiological systems by taking into account heat transfer mech-anisms such as thermal conduction blood perfusion andmetabolic heat generation [34 35] This model had somecritics in past decades as it does not consider the effectof blood flow direction it considers that heat equilibrationhappens in the capillaries and does not take into account theblood leaving the tissue Several studies tried to overcomethese limitations [36ndash38] but it is still widely used by themajority of papers in the literature due to its implementationsimplicity and availability of its parameters experimentallyThe Pennes bioheat transfer equation [34 35] is given by

120588119862119875 120597119879120597119905 = K sdot (12059721198791205971199092 +

12059721198791205971199102 ) + 120596119887120588119887119862119901119887 (119879119886 minus 119879)

+ 119876119898(1)

where 120588 [Kgm3] is the density of tissue 119862119875 [J (Kg ∘C)]is the specific heat of tissue K [W(m ∘C)] is the thermalconductivity 120596119887 [ml (sml)] is the blood perfusion rate 120588119887[Kgm3] is the density of blood119862119901119887 [J(Kg ∘C)] is the specific

International Journal of Biomedical Imaging 3

Table 1 Thermal properties used for temperature simulation

Material Property namek [W(m ∘C)] 120588 [1198961198921198983] 119862119901 [119869(119870119892 ∘119862)] 119876119898 [1198821198983] 120596119887 [119898119897(119898119897 ∙ 119904)] Refs

CSF 06 1000 4200 0 0 [43]GM 0565 10355 3680 16229 0013289 [43]WM 0503 10274 3600 45179 00036956 [43]Tumor 0565 10274 3600 25000 00005 [31 43 44]

heat of blood 119879119886 [∘C] is the temperature of artery and 119876119898[Wm3] is metabolic heat generation The left-hand side of(1) represents the stored heat energy the second describesthe heat transfer due to conduction the second term refersto the temperature exchange between the blood and thesurrounding tissue due to blood convection and the lastterm denotes the heat generation by cellular metabolism

To solve (1) normal body temperature 119879119894 = 37∘C is con-sidered as the initial condition In the boundary conditionsbrain tissues are supposed to be exposed to constant normalbody temperature 119879119887 = 37∘C [39] The thermal propertiesof blood perfusion were consigned as 120588119887= 1052 [Kgm3]119862119901119887= 3800 J (Kg ∘C) and 119879119886= 37∘C [40] Finite differencemethod was applied for discretization of Pennes bioheattransfer equation within the Cartesian grid where i and jrepresent the pixel index in the image space coordinate Thetime step was assumed at Δ119905 = 01119904 and the spatial stepΔ119909 = Δ119910 = 1 119898119898 which is derived from image resolutionThe solver convergence was assumed when the temperaturedifference within all image pixels between two consecutiveiterations is less than 1 sdot 10minus7 The approximation of thesecond derivative with respect to both time and space usingthe finite differences is descripted as follows [41]

(120597119879120597119905 )119894119895 =

119879119899+1119894119895 minus 119879119899119894119895Δ119905 (2)

(12059721198791205971199092 )

119894119895

= 119879119894minus1119895 minus 2119879119894119895 + 119879119894+1119895Δ1199092 (3)

(12059721198791205971199102 )

119894119895

= 119879119894119895minus1 minus 2119879119894119895 + 119879119894119895+1Δ1199092 (4)

After discretization using finite difference method (1) be-comes [42]

119879119899+1119894119895= 119879119899119894119895 + Δ119905K

120588119894119895119862119894119895Δ1199092sdot [119879119899119894minus1119895 + 119879119899119894+1119895 + 119879119899119894119895minus1 + 119879119899119894119895+1 minus 4119879119899119894119895]+ Δ119905

120588119894119895119862119894119895 [(120596119887)119894119895 (120588119887)119894119895 (119862119875119887)119894119895 (119879119899119886 minus 119879119899119894119895) + 119876119894119895]

(5)

Table 1 presents the thermal properties used for tem-perature simulations of normal brain tissues and tumor A

tumor with 120596119861 = 00016 119878minus1 and 119876119898 = 25000 [Wm3] isconsidered in this study

Towards the stability and convergence of (5) Δt shouldsatisfy the inequality as follows [42]

Δ119905 le 2Δx2120588119862119875120596119887120588119887119862119901119887Δx2 + 12119896 (6)

22 ChanndashVese Model Towards the segmentation of braintumors in T1 contrast and Flair MRI images we haveused active contours without edges proposed by Chan andVese [45] which is an energy-based method based on theMumford-Shah segmentation method [46] by approximatingthe image pixels intensities inside and outside the curveknown as c1 and c2 respectively The minimization problemof energy functional defined by Chan and Vese is describedin the following formula

119865119862119881 (1198881 1198882C)= 120583 ∙ int

Ω120575 (Φ (119909 119910)) 1003816100381610038161003816nablaΦ (119909 119910)1003816100381610038161003816 119889119909119889119910 + V

∙ intΩ119867(Φ (119909 119910)) 119889119909119889119910

+ 1205821 intΩ

10038161003816100381610038161199060 (119909 119910) minus 119888110038161003816100381610038162119867(Φ (119909 119910)) 119889119909119889119910+ 1205822 int

Ω

10038161003816100381610038161199060 (119909 119910) minus 119888210038161003816100381610038162 (1 minus 119867 (Φ (119909 119910))) 119889119909119889119910

(7)

where 120583 1205821 and 1205822 are positives parameters in this paperthey were initialized at 05 1 and 2 respectively [47] Φ islevel set function 1199060(119909 119910) is the input image C is the curvewhich corresponding to zero level set function Φ and H isthe Heaviside function [45]

119867(119911) = 1 119894119891 119911 ge 00 119894119891 119911 lt 0 (8)

and 120575 is one-dimensional Dirac measure [45]

120575 (119911) = 119889119889119911119867 (119911) (9)

while 120601 (x y) is a signed distance function defined as [45]

Φ(119909 119910) gt 0 119894119891 (119909 119910) 120598 119868119899119904119894119889119890 (119862)Φ (119909 119910) = 0 119894119891 (119909 119910) 120598 119874119899 (119862)Φ (119909 119910) lt 0 119894119891 (119909 119910) 120598 119874119906119905119904119894119889119890 (119862)

(10)

4 International Journal of Biomedical Imaging

To solve this minimization problem of the energy func-tional in (7) the gradient descent method is used to deriveEulerndashLagrange equations and update the level set functions[45]

120597Φ120597119905 = 120575 (Φ) [120583119889119894V( nablaΦ

|nablaΦ|) minus V minus 1205821 (1199060 minus 1198881)2

+ 1205822 (1199060 minus 1198882)2](11)

where c1 and c2 are defined as follows [45]

1198881 (Φ) = intΩ1199060 (119909 119910)119867 (Φ (119909 119910)) 119889119909119889119910intΩ119867(Φ (119909 119910)) 119889119909119889119910

1198882 (Φ) = intΩ1199060 (119909 119910) [1 minus 119867 (Φ (119909 119910))] 119889119909119889119910intΩ[1 minus 119867 (Φ (119909 119910))] 119889119909119889119910

(12)

and we implemented level set method based on the work ofCrandall [47]

23 Canny Edge Detector The calculated temperature dis-tribution (thermal image) in this study showed that a largegradient in tumor borders is the reason to use an edgedetection method to track the tumor contours An edgein the image represents a strong local variation in pixelsintensity usually arising on the boundary between twodifferent regions within an image Edge detection is theprocess of objects boundaries detection within an image byfinding the changes in discontinuities intensities There areseveral edge detection methods developed in the literatureThe most famous methods are the edge detection operatorsof Roberts Sobel Prewitt Kirsh Marr-Hildreth RobinsonLoG and Canny and so on Here in this work to detecttumor contours based on temperature distribution Cannyedge detection method [48] was used as it provides muchbetter results with strong edges compared with the other edgedetectionmethods Canny is based on amultistage algorithmIt consists of five separate steps smoothing gradient findingnonmaximumsuppression double threshold and edge track-ing using hysteresis Due to the addition of noise in thermalimages the smoothing step was applied two times

24 Segmentation Evaluation To evaluate the performance ofbrain tumor segmentation we have used five metrics Accu-racy Sensitivity Specificity Dice Coefficient and Jaccardcoefficient which are computed according to the following[49]

Accuracy = 119879119875 + 119879119873119879119875 + 119865119875 + 119879119873 + 119865119873 (13)

Sensitivity = 119879119875119879119875 + 119865119873 (14)

Specificity = 119879119873119879119873 + 119865119875 (15)

Dice = 21198791198752119879119875 + 119865119875 + 119865119873 (16)

Jaccard = 119879119875119879119875 + 119865119875 + 119865119873 (17)

where TP ldquoTrue Positiverdquo counts the number of pixels that arecorrectly segmented as a tumor and FP ldquoFalse Positiverdquo rep-resents the number of pixels in the image that are incorrectlysegmented as a tumor FN ldquoFalse Negativerdquo gives the numberof pixels that are incorrectly segmented as healthy pixels andTN stands for ldquoTrue Positiverdquo denotes the number of pixelsthat are correctly segmented as healthy pixels

25 Experiments on Synthetic MRI Images To validate theapproach in tumors with different locations and volumes wehave taken four synthetic MRI images of patients with braintumors from [50] with 116 1198881198983 for patient 1 274 1198881198983 forpatient 2 511 1198881198983 for patient 3 and 817 1198881198983 for patient4 The tumors were generated using realistic 3D tumorgrowth cross-platform software called TumorSim simulator[51] It was used to validate brain tumor segmentation inmany recent papers [52ndash54] The database containing 100MRI images of brains with tumors of different locationsand volumes was created from 20 patients in the BrainWebdatabase five images per patient [55] Each generated MRimage includes 181 slices Figure 1 shows that the T1 T1contrast-enhanced and FLAIR slices were taken from fourdifferent patients with different tumor volumes in the axialplane associated with its ground truth The 2D axial planesare presented for varying brain patients Each one of the slicesis taken at the axial plane of maximum tumor surface Allthe images are 256 x 256 pixels 12ndashbit grayscale in DICOMformat and have 1 mm2 isotropic resolution

Fifty other synthetic patients were used to test ourapproach 25 patients with high-grade tumors were takenfrom BRATS 2012 Training data 25 other patients with low-grade tumors were taken from BRATS 2013 Training data[56 57] these synthetic images were created using TumorSim[51] The reason of validating the approach in synthetic MRIimages is that it contains the ground truth of brain tissues andtumors therefore we take the real geometries of tissues andtumors for more accurate temperature calculation

The bioheat transfer equation level set method andCanny edge detection method were implemented usingCC++ on Windows 7 operating system with a CPU Inteli7-4770k The CC++ code has been compiled with VisualC++ compiler The DICOM images were read using ITK(wwwitkorg) library

3 Results and Discussion

Brain tumor thermally represents a heat source its volumeaffects temperature distribution A simplified circular tumorwith three diameters is placed in the same location in thehealthy brain as shown in Figure 2 At first glance we canobserve that temperature increases in tumors with highersizes (volume) For tumors with 10 mm 15 mm and 20 mmthe temperature increases with 058∘C 099∘C and 137∘C

International Journal of Biomedical Imaging 3

Table 1 Thermal properties used for temperature simulation

Material Property namek [W(m ∘C)] 120588 [1198961198921198983] 119862119901 [119869(119870119892 ∘119862)] 119876119898 [1198821198983] 120596119887 [119898119897(119898119897 ∙ 119904)] Refs

CSF 06 1000 4200 0 0 [43]GM 0565 10355 3680 16229 0013289 [43]WM 0503 10274 3600 45179 00036956 [43]Tumor 0565 10274 3600 25000 00005 [31 43 44]

heat of blood 119879119886 [∘C] is the temperature of artery and 119876119898[Wm3] is metabolic heat generation The left-hand side of(1) represents the stored heat energy the second describesthe heat transfer due to conduction the second term refersto the temperature exchange between the blood and thesurrounding tissue due to blood convection and the lastterm denotes the heat generation by cellular metabolism

To solve (1) normal body temperature 119879119894 = 37∘C is con-sidered as the initial condition In the boundary conditionsbrain tissues are supposed to be exposed to constant normalbody temperature 119879119887 = 37∘C [39] The thermal propertiesof blood perfusion were consigned as 120588119887= 1052 [Kgm3]119862119901119887= 3800 J (Kg ∘C) and 119879119886= 37∘C [40] Finite differencemethod was applied for discretization of Pennes bioheattransfer equation within the Cartesian grid where i and jrepresent the pixel index in the image space coordinate Thetime step was assumed at Δ119905 = 01119904 and the spatial stepΔ119909 = Δ119910 = 1 119898119898 which is derived from image resolutionThe solver convergence was assumed when the temperaturedifference within all image pixels between two consecutiveiterations is less than 1 sdot 10minus7 The approximation of thesecond derivative with respect to both time and space usingthe finite differences is descripted as follows [41]

(120597119879120597119905 )119894119895 =

119879119899+1119894119895 minus 119879119899119894119895Δ119905 (2)

(12059721198791205971199092 )

119894119895

= 119879119894minus1119895 minus 2119879119894119895 + 119879119894+1119895Δ1199092 (3)

(12059721198791205971199102 )

119894119895

= 119879119894119895minus1 minus 2119879119894119895 + 119879119894119895+1Δ1199092 (4)

After discretization using finite difference method (1) be-comes [42]

119879119899+1119894119895= 119879119899119894119895 + Δ119905K

120588119894119895119862119894119895Δ1199092sdot [119879119899119894minus1119895 + 119879119899119894+1119895 + 119879119899119894119895minus1 + 119879119899119894119895+1 minus 4119879119899119894119895]+ Δ119905

120588119894119895119862119894119895 [(120596119887)119894119895 (120588119887)119894119895 (119862119875119887)119894119895 (119879119899119886 minus 119879119899119894119895) + 119876119894119895]

(5)

Table 1 presents the thermal properties used for tem-perature simulations of normal brain tissues and tumor A

tumor with 120596119861 = 00016 119878minus1 and 119876119898 = 25000 [Wm3] isconsidered in this study

Towards the stability and convergence of (5) Δt shouldsatisfy the inequality as follows [42]

Δ119905 le 2Δx2120588119862119875120596119887120588119887119862119901119887Δx2 + 12119896 (6)

22 ChanndashVese Model Towards the segmentation of braintumors in T1 contrast and Flair MRI images we haveused active contours without edges proposed by Chan andVese [45] which is an energy-based method based on theMumford-Shah segmentation method [46] by approximatingthe image pixels intensities inside and outside the curveknown as c1 and c2 respectively The minimization problemof energy functional defined by Chan and Vese is describedin the following formula

119865119862119881 (1198881 1198882C)= 120583 ∙ int

Ω120575 (Φ (119909 119910)) 1003816100381610038161003816nablaΦ (119909 119910)1003816100381610038161003816 119889119909119889119910 + V

∙ intΩ119867(Φ (119909 119910)) 119889119909119889119910

+ 1205821 intΩ

10038161003816100381610038161199060 (119909 119910) minus 119888110038161003816100381610038162119867(Φ (119909 119910)) 119889119909119889119910+ 1205822 int

Ω

10038161003816100381610038161199060 (119909 119910) minus 119888210038161003816100381610038162 (1 minus 119867 (Φ (119909 119910))) 119889119909119889119910

(7)

where 120583 1205821 and 1205822 are positives parameters in this paperthey were initialized at 05 1 and 2 respectively [47] Φ islevel set function 1199060(119909 119910) is the input image C is the curvewhich corresponding to zero level set function Φ and H isthe Heaviside function [45]

119867(119911) = 1 119894119891 119911 ge 00 119894119891 119911 lt 0 (8)

and 120575 is one-dimensional Dirac measure [45]

120575 (119911) = 119889119889119911119867 (119911) (9)

while 120601 (x y) is a signed distance function defined as [45]

Φ(119909 119910) gt 0 119894119891 (119909 119910) 120598 119868119899119904119894119889119890 (119862)Φ (119909 119910) = 0 119894119891 (119909 119910) 120598 119874119899 (119862)Φ (119909 119910) lt 0 119894119891 (119909 119910) 120598 119874119906119905119904119894119889119890 (119862)

(10)

4 International Journal of Biomedical Imaging

To solve this minimization problem of the energy func-tional in (7) the gradient descent method is used to deriveEulerndashLagrange equations and update the level set functions[45]

120597Φ120597119905 = 120575 (Φ) [120583119889119894V( nablaΦ

|nablaΦ|) minus V minus 1205821 (1199060 minus 1198881)2

+ 1205822 (1199060 minus 1198882)2](11)

where c1 and c2 are defined as follows [45]

1198881 (Φ) = intΩ1199060 (119909 119910)119867 (Φ (119909 119910)) 119889119909119889119910intΩ119867(Φ (119909 119910)) 119889119909119889119910

1198882 (Φ) = intΩ1199060 (119909 119910) [1 minus 119867 (Φ (119909 119910))] 119889119909119889119910intΩ[1 minus 119867 (Φ (119909 119910))] 119889119909119889119910

(12)

and we implemented level set method based on the work ofCrandall [47]

23 Canny Edge Detector The calculated temperature dis-tribution (thermal image) in this study showed that a largegradient in tumor borders is the reason to use an edgedetection method to track the tumor contours An edgein the image represents a strong local variation in pixelsintensity usually arising on the boundary between twodifferent regions within an image Edge detection is theprocess of objects boundaries detection within an image byfinding the changes in discontinuities intensities There areseveral edge detection methods developed in the literatureThe most famous methods are the edge detection operatorsof Roberts Sobel Prewitt Kirsh Marr-Hildreth RobinsonLoG and Canny and so on Here in this work to detecttumor contours based on temperature distribution Cannyedge detection method [48] was used as it provides muchbetter results with strong edges compared with the other edgedetectionmethods Canny is based on amultistage algorithmIt consists of five separate steps smoothing gradient findingnonmaximumsuppression double threshold and edge track-ing using hysteresis Due to the addition of noise in thermalimages the smoothing step was applied two times

24 Segmentation Evaluation To evaluate the performance ofbrain tumor segmentation we have used five metrics Accu-racy Sensitivity Specificity Dice Coefficient and Jaccardcoefficient which are computed according to the following[49]

Accuracy = 119879119875 + 119879119873119879119875 + 119865119875 + 119879119873 + 119865119873 (13)

Sensitivity = 119879119875119879119875 + 119865119873 (14)

Specificity = 119879119873119879119873 + 119865119875 (15)

Dice = 21198791198752119879119875 + 119865119875 + 119865119873 (16)

Jaccard = 119879119875119879119875 + 119865119875 + 119865119873 (17)

where TP ldquoTrue Positiverdquo counts the number of pixels that arecorrectly segmented as a tumor and FP ldquoFalse Positiverdquo rep-resents the number of pixels in the image that are incorrectlysegmented as a tumor FN ldquoFalse Negativerdquo gives the numberof pixels that are incorrectly segmented as healthy pixels andTN stands for ldquoTrue Positiverdquo denotes the number of pixelsthat are correctly segmented as healthy pixels

25 Experiments on Synthetic MRI Images To validate theapproach in tumors with different locations and volumes wehave taken four synthetic MRI images of patients with braintumors from [50] with 116 1198881198983 for patient 1 274 1198881198983 forpatient 2 511 1198881198983 for patient 3 and 817 1198881198983 for patient4 The tumors were generated using realistic 3D tumorgrowth cross-platform software called TumorSim simulator[51] It was used to validate brain tumor segmentation inmany recent papers [52ndash54] The database containing 100MRI images of brains with tumors of different locationsand volumes was created from 20 patients in the BrainWebdatabase five images per patient [55] Each generated MRimage includes 181 slices Figure 1 shows that the T1 T1contrast-enhanced and FLAIR slices were taken from fourdifferent patients with different tumor volumes in the axialplane associated with its ground truth The 2D axial planesare presented for varying brain patients Each one of the slicesis taken at the axial plane of maximum tumor surface Allthe images are 256 x 256 pixels 12ndashbit grayscale in DICOMformat and have 1 mm2 isotropic resolution

Fifty other synthetic patients were used to test ourapproach 25 patients with high-grade tumors were takenfrom BRATS 2012 Training data 25 other patients with low-grade tumors were taken from BRATS 2013 Training data[56 57] these synthetic images were created using TumorSim[51] The reason of validating the approach in synthetic MRIimages is that it contains the ground truth of brain tissues andtumors therefore we take the real geometries of tissues andtumors for more accurate temperature calculation

The bioheat transfer equation level set method andCanny edge detection method were implemented usingCC++ on Windows 7 operating system with a CPU Inteli7-4770k The CC++ code has been compiled with VisualC++ compiler The DICOM images were read using ITK(wwwitkorg) library

3 Results and Discussion

Brain tumor thermally represents a heat source its volumeaffects temperature distribution A simplified circular tumorwith three diameters is placed in the same location in thehealthy brain as shown in Figure 2 At first glance we canobserve that temperature increases in tumors with highersizes (volume) For tumors with 10 mm 15 mm and 20 mmthe temperature increases with 058∘C 099∘C and 137∘C

4 International Journal of Biomedical Imaging

To solve this minimization problem of the energy func-tional in (7) the gradient descent method is used to deriveEulerndashLagrange equations and update the level set functions[45]

120597Φ120597119905 = 120575 (Φ) [120583119889119894V( nablaΦ

|nablaΦ|) minus V minus 1205821 (1199060 minus 1198881)2

+ 1205822 (1199060 minus 1198882)2](11)

where c1 and c2 are defined as follows [45]

1198881 (Φ) = intΩ1199060 (119909 119910)119867 (Φ (119909 119910)) 119889119909119889119910intΩ119867(Φ (119909 119910)) 119889119909119889119910

1198882 (Φ) = intΩ1199060 (119909 119910) [1 minus 119867 (Φ (119909 119910))] 119889119909119889119910intΩ[1 minus 119867 (Φ (119909 119910))] 119889119909119889119910

(12)

and we implemented level set method based on the work ofCrandall [47]

23 Canny Edge Detector The calculated temperature dis-tribution (thermal image) in this study showed that a largegradient in tumor borders is the reason to use an edgedetection method to track the tumor contours An edgein the image represents a strong local variation in pixelsintensity usually arising on the boundary between twodifferent regions within an image Edge detection is theprocess of objects boundaries detection within an image byfinding the changes in discontinuities intensities There areseveral edge detection methods developed in the literatureThe most famous methods are the edge detection operatorsof Roberts Sobel Prewitt Kirsh Marr-Hildreth RobinsonLoG and Canny and so on Here in this work to detecttumor contours based on temperature distribution Cannyedge detection method [48] was used as it provides muchbetter results with strong edges compared with the other edgedetectionmethods Canny is based on amultistage algorithmIt consists of five separate steps smoothing gradient findingnonmaximumsuppression double threshold and edge track-ing using hysteresis Due to the addition of noise in thermalimages the smoothing step was applied two times

24 Segmentation Evaluation To evaluate the performance ofbrain tumor segmentation we have used five metrics Accu-racy Sensitivity Specificity Dice Coefficient and Jaccardcoefficient which are computed according to the following[49]

Accuracy = 119879119875 + 119879119873119879119875 + 119865119875 + 119879119873 + 119865119873 (13)

Sensitivity = 119879119875119879119875 + 119865119873 (14)

Specificity = 119879119873119879119873 + 119865119875 (15)

Dice = 21198791198752119879119875 + 119865119875 + 119865119873 (16)

Jaccard = 119879119875119879119875 + 119865119875 + 119865119873 (17)

where TP ldquoTrue Positiverdquo counts the number of pixels that arecorrectly segmented as a tumor and FP ldquoFalse Positiverdquo rep-resents the number of pixels in the image that are incorrectlysegmented as a tumor FN ldquoFalse Negativerdquo gives the numberof pixels that are incorrectly segmented as healthy pixels andTN stands for ldquoTrue Positiverdquo denotes the number of pixelsthat are correctly segmented as healthy pixels

25 Experiments on Synthetic MRI Images To validate theapproach in tumors with different locations and volumes wehave taken four synthetic MRI images of patients with braintumors from [50] with 116 1198881198983 for patient 1 274 1198881198983 forpatient 2 511 1198881198983 for patient 3 and 817 1198881198983 for patient4 The tumors were generated using realistic 3D tumorgrowth cross-platform software called TumorSim simulator[51] It was used to validate brain tumor segmentation inmany recent papers [52ndash54] The database containing 100MRI images of brains with tumors of different locationsand volumes was created from 20 patients in the BrainWebdatabase five images per patient [55] Each generated MRimage includes 181 slices Figure 1 shows that the T1 T1contrast-enhanced and FLAIR slices were taken from fourdifferent patients with different tumor volumes in the axialplane associated with its ground truth The 2D axial planesare presented for varying brain patients Each one of the slicesis taken at the axial plane of maximum tumor surface Allthe images are 256 x 256 pixels 12ndashbit grayscale in DICOMformat and have 1 mm2 isotropic resolution

Fifty other synthetic patients were used to test ourapproach 25 patients with high-grade tumors were takenfrom BRATS 2012 Training data 25 other patients with low-grade tumors were taken from BRATS 2013 Training data[56 57] these synthetic images were created using TumorSim[51] The reason of validating the approach in synthetic MRIimages is that it contains the ground truth of brain tissues andtumors therefore we take the real geometries of tissues andtumors for more accurate temperature calculation

The bioheat transfer equation level set method andCanny edge detection method were implemented usingCC++ on Windows 7 operating system with a CPU Inteli7-4770k The CC++ code has been compiled with VisualC++ compiler The DICOM images were read using ITK(wwwitkorg) library

3 Results and Discussion

Brain tumor thermally represents a heat source its volumeaffects temperature distribution A simplified circular tumorwith three diameters is placed in the same location in thehealthy brain as shown in Figure 2 At first glance we canobserve that temperature increases in tumors with highersizes (volume) For tumors with 10 mm 15 mm and 20 mmthe temperature increases with 058∘C 099∘C and 137∘C

International Journal of Biomedical Imaging 5

V = 116 cm3

Grey matter

White matterCerebrospinal fluid

Tumor

(a)

V = 274 cm3

Grey matter

White matterCerebrospinal fluid

Tumor

(b)

V = 511 cm3

Grey matter

White matterCerebrospinal fluid

Tumor

(c)

V = 817 cm3

Grey matter

White matterCerebrospinal fluid

Tumor

(d)

Figure 1 Synthetic T1 T1 contrast and Flair images of four patients with tumors of different volumes with their ground truth (a) Tumorwith 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

respectively where themaximum temperature is in the tumorcenter Figure 3 provides more clear representation of 1Dtemperature profile in the line passing through the center oftumors Results show clearly the existence of abnormalityalso the rises in temperature distribution do not only indicatethe existence of a tumor but also provide useful informationabout its localization

Next the temperatures distributions of the brain withrealistic tumorswere calculated with the addition of Gaussiannoise Figure 4 presents the synthetic images used for theanalysis of the proposed approach it gives the ground truthof four patients with different tumor volumes in differentlocations and illustrates the corresponding calculated tem-perature distributionThemaximum temperatures rise whichis in the center of tumors with volumes of 116 cm3 274 1198881198983511 1198881198983 and 817 1198881198983 being 186∘C 253∘C 275∘C and312∘C respectively without addition of noise The obtainedresults confirm that temperature is high with increasingtumor volumes

Figure 5 shows drawn temperature isotherm withoutnoise on T1-weighted images based on tumor temperatureprofile to analyze the degree of variation of temperature inthe tumorous region Six curves in different colors with 05∘Cof difference between each curve were drawn to representtemperature lines of 375∘C 38∘C 385∘C 39∘C 395∘C and40∘C respectively For the four patients the curve with375∘C of temperature represents healthy pixels Howeverthese pixels are affected by tumor temperature Also asthe tumor volume increases this curve moves away fromthe tumor borders as shown in Figures 2 and 4 tumorswith high volume generate more heat compared to tumorswith fewer volumes Notice that not only the temperatureincreases but also it has a larger distribution this explainsthat curve with 375∘C is located far from contours in tumorsof high volume We also observe that temperature has anabrupt change in the tumor contours compared to tumorcore and the healthy area this observation is confirmed inFigure 6 which shows a 1D absolute gradient in the line

6 International Journal of Biomedical Imaging

Grey matter

White matterCerebrospinal fluid

Tumor

37 372 374 376 378 38 382 384 386

Temperature (∘C)

(a)

Grey matter

White matterCerebrospinal fluid

Tumor

372 374 376 378 38 382 384 386

Temperature (∘C)37

(b)

Grey matter

White matterCerebrospinal fluid

Tumor

372 374 376 378 38 382 384 386

Temperature (∘C)37

(c)

Figure 2 Temperature distribution of brain with circular tumors of three different diameters (a) Tumor with 10 mm of diameter (b) Tumorwith 15 mm of diameter (c) Tumor with 20 mm of diameter

d3 = 20 mmd2 = 15 mmd1 = 10 mmWithout tumor

37

372

374

376

378

38

382

384

386

388

Tem

pera

ture

(∘C)

20 40 60 80 100 1200Distance (mm)

Figure 3 1D representation of temperature profile on the path passes through the tumors centers with different sizes

International Journal of Biomedical Imaging 7

37 375 38 385 39

Temperature (∘C)

(a)

395 4037 375 38 385 39

Temperature (∘C)

(b)

395 4037 375 38 385 39

Temperature (∘C)

(c)

395 4054037 375 38 385 39

Temperature (∘C)

(d)

Figure 4 Temperature distribution with noise of brains with realistic tumors of different volumes (a) Tumor with 116 cm3 of volume (b)Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

(a)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

(b)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

T = 40∘C

(c)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

T = 40∘C

(d)

Figure 5 Temperature isotherms in the four cases to show the degree of variation of temperature in the tumorous region

8 International Journal of Biomedical Imaging

GradientTumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(a)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(b)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(c)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(d)

Figure 6 1D representation of temperature absolute gradient on the path passes in the tumor center in the four cases (a) Tumor with 116cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

passing across the tumor center It can be inferred that thetemperature gradient always has the maximum value in thetumor contours for all the four patients which proves thattumor thermal profile can provide rich information abouttumor borders that can be used to reinforce segmentationalgorithms This assumption represents the basis on whichour approachhas beendeveloped usingCanny edge detectionmethod to detect tumor contours from temperature distribu-tion

The results of the segmentation are illustrated in Figure 7where green and red curves represent segmentation andground truth respectively Each patient is presented in

different column the two first lines present the results ofsegmentation using the level set method on different MRIsequences which are T1 contrast and Flair respectively Thelast line gives the obtained results of segmentation usingthe proposed approach based on Canny edge detection fromtemperature distribution with the addition of Gaussian noiseshown in T1-weighted images At first glance it can beinferred that segmentation has been improved significantlyusing the proposed approach These results demonstratethat temperature provides rich information about tumormargins that can be exploited to have more effective tumorsegmentation in MRI images

International Journal of Biomedical Imaging 9

(Patient 1) (Patient 2) (Patient 3) (Patient 4)

Figure 7 Results of segmentation by level set method in MRI images and the proposed approach The first and second lines provide thesegmentation by level set in T1 contrast and Flair respectively The last line gives the segmentation using the proposed approach showed inT1 Green segmentation Red ground truth

To evaluate the segmentation performance we have usedfive metrics Accuracy Sensitivity Specificity Dice Coeffi-cient and the Jaccard coefficient The obtained results arepresented in Table 2 it presents the calculated segmenta-tion evaluation metrics for level set method in differentMRI sequences and the proposed approach The proposedapproach yields good results compared with level set segmen-tation for the four patients All the metrics were improvedin all cases except Sensitivity which is reduced It can bejustified by the fact that in the segmentation in MRI usinglevel set the number of FN is very weak and sometimesnull as most of the curve of the level set remains outside thetumorous area

The temperature is obtained using the Pennes bioheatequation which can produce errors in calculation comparedto the experimentally measured temperature as the model isisotropic and the used thermal properties donot represent therealistic properties of the patientHowever our interest in thiswork is the way the temperature is diffused and its variationin tumor borders independently of the degree of temperaturerises in the tumorous region Figure 8 shows a one-dimensiontemperature distribution with noise in the tumorous regionsby considering three different values of blood perfusionrate which are 0001 119878minus1 00016 119878minus1 and 0002 119878minus1 for thesame tumor Table 3 furnishes the calculated segmentationevaluation metrics for the proposed approach using Cannymethod applied on three temperature profiles of the sametumor for all patients It can be inferred that the obtainedresults of segmentation are with good accuracy for all cases

In addition there is no significant difference by applying theapproach on three tumor temperature profiles of the sametumor as there is a high variation of temperature in tumorborders in the three cases which proves more the feasibilityand the robustness of the proposed approach

Thus far tumors contours were detected using steady-state thermal analysis where the segmentation was per-formed in the equilibrium state of temperature distributionIn order to study the effect of transient thermal analysis inbrain tumors segmentation cold stress was applied Froman initial temperature distribution at thermal equilibriumobtained using (5) a cold stress temperature 119879119888119900119897119889 wasreducing from each pixel temperature We have consideredthree values for 119879119888119900119897119889 025∘C 05∘C and 1∘C After coolingthe brain (5) was then solved Next thermal images wereobtained at different time steps 5 s 100 s 600 s 1000 s2000 s and 2500 s respectively Table 4 depicts the obtainedresults of segmentation evaluation of the obtained thermalimages at each time step It can be observed that in the threecases for all times steps the approach is still giving acceptableresults which shows the applicability of the approach even intransient temperature distribution

Figures 9 and 10 depict the results of segmentation of levelset in Flair images and the proposed approach in the thermalimages with additional noise of fifty synthetic patients takenfrom BRATS 2012 Training data and BRATS Training datarespectively Tables 5 and 6 report the performance evaluationof the 50 patients In all tested cases the delineation oftumor contours based on temperature distribution showed a

10 International Journal of Biomedical Imaging

Table 2 The calculated segmentation evaluation metrics for level set method and proposed approach

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1T1c 593 191 14609 0 1 0987 09875 08612 07563Flair 592 285 14515 1 09983 09807 09814 08054 06742

Thermal map 556 5 14795 37 09376 09996 09972 09636 09297

Patient 2T1c 1160 287 18241 0 1 09845 09854 08899 08016Flair 1138 369 18159 22 0981 098 09801 08533 07442

Thermal map 1112 37 18491 48 09586 0998 09956 09631 09289

Patient 3T1c 1593 105 18116 28 09827 09942 09932 09599 09229Flair 1598 431 17790 23 09858 09763 09771 08756 07787

Thermal map 1547 7 18214 74 09543 09996 09959 09744 09502

Patient 4T1c 2224 109 17545 204 09159 09938 09844 09342 08766Flair 2428 418 17236 0 1 09763 09791 09207 08531

Thermal map 2375 25 17629 53 09781 09985 09961 09838 09682

Table 3 The calculated segmentation evaluation metrics for the proposed approach by considering different values of blood perfusion rate

Patient No 120596119887 TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 10001 549 3 14797 44 09258 09997 09969 09589 0921100016 556 5 14795 37 09376 09996 09972 09636 092970002 559 5 14795 34 09426 09996 09974 09662 09347

Patient 20001 1099 29 18499 61 09474 09984 09954 09606 0924300016 1112 37 18491 48 09586 0998 09956 09631 092890002 1118 47 18481 42 09637 09974 09954 09617 09262

Patient 30001 1538 1 18220 83 09487 09999 09957 09734 0948200016 1547 7 18214 74 09543 09996 09959 09744 095020002 1558 14 18207 63 09611 09992 09961 09758 09529

Patient 40001 2330 17 17637 98 09596 0999 09942 09759 0952900016 2375 25 17629 53 09781 09985 09961 09838 096820002 2387 31 17623 41 09831 09982 09964 09851 09707

Table 4 The calculated segmentation evaluation metrics for transient thermal analysis in brain tumor contours detection

T119888119900119897119889 (∘C) Time (s) Sensitivity Specificity Accuracy Dice index Jaccard

025

5 09773 09986 0996 09836 09677100 09761 0999 09963 09846 09697600 09794 09985 09962 09842 09691000 09781 09985 09961 09838 0968220000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

05

5 09744 09986 09957 09823 09653100 09703 09993 09958 09826 09659600 0981 09986 09965 09855 097141000 09794 09985 09962 09842 096920000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

10

5 09707 0997 09938 09745 09504100 0953 09994 09938 09741 09495600 09831 09987 09968 09869 097421000 0918 09986 09889 09525 0909420000 09785 09985 09961 0984 0968625000 09781 09985 09961 09838 09682

International Journal of Biomedical Imaging 11

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

20 40 60 80 100 1200Distance (mm)

37

375

38

385

39

395

40

Tem

pera

ture

(∘C)

(a)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(b)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

415

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(c)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

36

37

38

39

40

41

42

43

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(d)

Figure 8 Temperature distribution with noise of brains with realistic tumors of different volumes by considering three values of bloodperfusion rate (a) Tumor with 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with817 cm3 of volume

significant improvement compared to the level set methodexcept Sensitivity and the reason is explained in the previouscase Table 7 presents the percent of the tumor and healthyareas differentiated by segmentation in thermal images onlyin all test cases from the two tables it can be observed thatsegmentation is reinforced using thermal images which leadto more effective segmentation of brain tumors

In Figure 9 and Table 5 patient 4 and patient 5 showfewer values of the used segmentation evaluation metrics(Dice Jaccard etc) using the proposed approach this

assumption is also observed in Figure 10 and Table 6 forpatients 2 and patient 6 This can be explained as thetemperature is calculated using the standard Pennes equationwhich is isotropic model and biological tissues are highlyanisotropic Accordingly the edge detected byCannymethodis smooth and finds difficulties in estimating complex geome-tries In future works we plan to modify the standard Pennesequation to consider anisotropy using MRI Diffusion TensorImaging (DTI) to guide the anisotropy in order to obtainmore accurate and realistic temperature distribution in the

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

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6 International Journal of Biomedical Imaging

Grey matter

White matterCerebrospinal fluid

Tumor

37 372 374 376 378 38 382 384 386

Temperature (∘C)

(a)

Grey matter

White matterCerebrospinal fluid

Tumor

372 374 376 378 38 382 384 386

Temperature (∘C)37

(b)

Grey matter

White matterCerebrospinal fluid

Tumor

372 374 376 378 38 382 384 386

Temperature (∘C)37

(c)

Figure 2 Temperature distribution of brain with circular tumors of three different diameters (a) Tumor with 10 mm of diameter (b) Tumorwith 15 mm of diameter (c) Tumor with 20 mm of diameter

d3 = 20 mmd2 = 15 mmd1 = 10 mmWithout tumor

37

372

374

376

378

38

382

384

386

388

Tem

pera

ture

(∘C)

20 40 60 80 100 1200Distance (mm)

Figure 3 1D representation of temperature profile on the path passes through the tumors centers with different sizes

International Journal of Biomedical Imaging 7

37 375 38 385 39

Temperature (∘C)

(a)

395 4037 375 38 385 39

Temperature (∘C)

(b)

395 4037 375 38 385 39

Temperature (∘C)

(c)

395 4054037 375 38 385 39

Temperature (∘C)

(d)

Figure 4 Temperature distribution with noise of brains with realistic tumors of different volumes (a) Tumor with 116 cm3 of volume (b)Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

(a)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

(b)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

T = 40∘C

(c)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

T = 40∘C

(d)

Figure 5 Temperature isotherms in the four cases to show the degree of variation of temperature in the tumorous region

8 International Journal of Biomedical Imaging

GradientTumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(a)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(b)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(c)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(d)

Figure 6 1D representation of temperature absolute gradient on the path passes in the tumor center in the four cases (a) Tumor with 116cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

passing across the tumor center It can be inferred that thetemperature gradient always has the maximum value in thetumor contours for all the four patients which proves thattumor thermal profile can provide rich information abouttumor borders that can be used to reinforce segmentationalgorithms This assumption represents the basis on whichour approachhas beendeveloped usingCanny edge detectionmethod to detect tumor contours from temperature distribu-tion

The results of the segmentation are illustrated in Figure 7where green and red curves represent segmentation andground truth respectively Each patient is presented in

different column the two first lines present the results ofsegmentation using the level set method on different MRIsequences which are T1 contrast and Flair respectively Thelast line gives the obtained results of segmentation usingthe proposed approach based on Canny edge detection fromtemperature distribution with the addition of Gaussian noiseshown in T1-weighted images At first glance it can beinferred that segmentation has been improved significantlyusing the proposed approach These results demonstratethat temperature provides rich information about tumormargins that can be exploited to have more effective tumorsegmentation in MRI images

International Journal of Biomedical Imaging 9

(Patient 1) (Patient 2) (Patient 3) (Patient 4)

Figure 7 Results of segmentation by level set method in MRI images and the proposed approach The first and second lines provide thesegmentation by level set in T1 contrast and Flair respectively The last line gives the segmentation using the proposed approach showed inT1 Green segmentation Red ground truth

To evaluate the segmentation performance we have usedfive metrics Accuracy Sensitivity Specificity Dice Coeffi-cient and the Jaccard coefficient The obtained results arepresented in Table 2 it presents the calculated segmenta-tion evaluation metrics for level set method in differentMRI sequences and the proposed approach The proposedapproach yields good results compared with level set segmen-tation for the four patients All the metrics were improvedin all cases except Sensitivity which is reduced It can bejustified by the fact that in the segmentation in MRI usinglevel set the number of FN is very weak and sometimesnull as most of the curve of the level set remains outside thetumorous area

The temperature is obtained using the Pennes bioheatequation which can produce errors in calculation comparedto the experimentally measured temperature as the model isisotropic and the used thermal properties donot represent therealistic properties of the patientHowever our interest in thiswork is the way the temperature is diffused and its variationin tumor borders independently of the degree of temperaturerises in the tumorous region Figure 8 shows a one-dimensiontemperature distribution with noise in the tumorous regionsby considering three different values of blood perfusionrate which are 0001 119878minus1 00016 119878minus1 and 0002 119878minus1 for thesame tumor Table 3 furnishes the calculated segmentationevaluation metrics for the proposed approach using Cannymethod applied on three temperature profiles of the sametumor for all patients It can be inferred that the obtainedresults of segmentation are with good accuracy for all cases

In addition there is no significant difference by applying theapproach on three tumor temperature profiles of the sametumor as there is a high variation of temperature in tumorborders in the three cases which proves more the feasibilityand the robustness of the proposed approach

Thus far tumors contours were detected using steady-state thermal analysis where the segmentation was per-formed in the equilibrium state of temperature distributionIn order to study the effect of transient thermal analysis inbrain tumors segmentation cold stress was applied Froman initial temperature distribution at thermal equilibriumobtained using (5) a cold stress temperature 119879119888119900119897119889 wasreducing from each pixel temperature We have consideredthree values for 119879119888119900119897119889 025∘C 05∘C and 1∘C After coolingthe brain (5) was then solved Next thermal images wereobtained at different time steps 5 s 100 s 600 s 1000 s2000 s and 2500 s respectively Table 4 depicts the obtainedresults of segmentation evaluation of the obtained thermalimages at each time step It can be observed that in the threecases for all times steps the approach is still giving acceptableresults which shows the applicability of the approach even intransient temperature distribution

Figures 9 and 10 depict the results of segmentation of levelset in Flair images and the proposed approach in the thermalimages with additional noise of fifty synthetic patients takenfrom BRATS 2012 Training data and BRATS Training datarespectively Tables 5 and 6 report the performance evaluationof the 50 patients In all tested cases the delineation oftumor contours based on temperature distribution showed a

10 International Journal of Biomedical Imaging

Table 2 The calculated segmentation evaluation metrics for level set method and proposed approach

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1T1c 593 191 14609 0 1 0987 09875 08612 07563Flair 592 285 14515 1 09983 09807 09814 08054 06742

Thermal map 556 5 14795 37 09376 09996 09972 09636 09297

Patient 2T1c 1160 287 18241 0 1 09845 09854 08899 08016Flair 1138 369 18159 22 0981 098 09801 08533 07442

Thermal map 1112 37 18491 48 09586 0998 09956 09631 09289

Patient 3T1c 1593 105 18116 28 09827 09942 09932 09599 09229Flair 1598 431 17790 23 09858 09763 09771 08756 07787

Thermal map 1547 7 18214 74 09543 09996 09959 09744 09502

Patient 4T1c 2224 109 17545 204 09159 09938 09844 09342 08766Flair 2428 418 17236 0 1 09763 09791 09207 08531

Thermal map 2375 25 17629 53 09781 09985 09961 09838 09682

Table 3 The calculated segmentation evaluation metrics for the proposed approach by considering different values of blood perfusion rate

Patient No 120596119887 TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 10001 549 3 14797 44 09258 09997 09969 09589 0921100016 556 5 14795 37 09376 09996 09972 09636 092970002 559 5 14795 34 09426 09996 09974 09662 09347

Patient 20001 1099 29 18499 61 09474 09984 09954 09606 0924300016 1112 37 18491 48 09586 0998 09956 09631 092890002 1118 47 18481 42 09637 09974 09954 09617 09262

Patient 30001 1538 1 18220 83 09487 09999 09957 09734 0948200016 1547 7 18214 74 09543 09996 09959 09744 095020002 1558 14 18207 63 09611 09992 09961 09758 09529

Patient 40001 2330 17 17637 98 09596 0999 09942 09759 0952900016 2375 25 17629 53 09781 09985 09961 09838 096820002 2387 31 17623 41 09831 09982 09964 09851 09707

Table 4 The calculated segmentation evaluation metrics for transient thermal analysis in brain tumor contours detection

T119888119900119897119889 (∘C) Time (s) Sensitivity Specificity Accuracy Dice index Jaccard

025

5 09773 09986 0996 09836 09677100 09761 0999 09963 09846 09697600 09794 09985 09962 09842 09691000 09781 09985 09961 09838 0968220000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

05

5 09744 09986 09957 09823 09653100 09703 09993 09958 09826 09659600 0981 09986 09965 09855 097141000 09794 09985 09962 09842 096920000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

10

5 09707 0997 09938 09745 09504100 0953 09994 09938 09741 09495600 09831 09987 09968 09869 097421000 0918 09986 09889 09525 0909420000 09785 09985 09961 0984 0968625000 09781 09985 09961 09838 09682

International Journal of Biomedical Imaging 11

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

20 40 60 80 100 1200Distance (mm)

37

375

38

385

39

395

40

Tem

pera

ture

(∘C)

(a)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(b)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

415

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(c)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

36

37

38

39

40

41

42

43

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(d)

Figure 8 Temperature distribution with noise of brains with realistic tumors of different volumes by considering three values of bloodperfusion rate (a) Tumor with 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with817 cm3 of volume

significant improvement compared to the level set methodexcept Sensitivity and the reason is explained in the previouscase Table 7 presents the percent of the tumor and healthyareas differentiated by segmentation in thermal images onlyin all test cases from the two tables it can be observed thatsegmentation is reinforced using thermal images which leadto more effective segmentation of brain tumors

In Figure 9 and Table 5 patient 4 and patient 5 showfewer values of the used segmentation evaluation metrics(Dice Jaccard etc) using the proposed approach this

assumption is also observed in Figure 10 and Table 6 forpatients 2 and patient 6 This can be explained as thetemperature is calculated using the standard Pennes equationwhich is isotropic model and biological tissues are highlyanisotropic Accordingly the edge detected byCannymethodis smooth and finds difficulties in estimating complex geome-tries In future works we plan to modify the standard Pennesequation to consider anisotropy using MRI Diffusion TensorImaging (DTI) to guide the anisotropy in order to obtainmore accurate and realistic temperature distribution in the

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

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Submit your manuscripts atwwwhindawicom

International Journal of Biomedical Imaging 7

37 375 38 385 39

Temperature (∘C)

(a)

395 4037 375 38 385 39

Temperature (∘C)

(b)

395 4037 375 38 385 39

Temperature (∘C)

(c)

395 4054037 375 38 385 39

Temperature (∘C)

(d)

Figure 4 Temperature distribution with noise of brains with realistic tumors of different volumes (a) Tumor with 116 cm3 of volume (b)Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

(a)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

(b)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

T = 40∘C

(c)

T = 375∘C

T = 38∘C

T = 385∘C

T = 39∘C

T = 395∘C

T = 40∘C

(d)

Figure 5 Temperature isotherms in the four cases to show the degree of variation of temperature in the tumorous region

8 International Journal of Biomedical Imaging

GradientTumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(a)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(b)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(c)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(d)

Figure 6 1D representation of temperature absolute gradient on the path passes in the tumor center in the four cases (a) Tumor with 116cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

passing across the tumor center It can be inferred that thetemperature gradient always has the maximum value in thetumor contours for all the four patients which proves thattumor thermal profile can provide rich information abouttumor borders that can be used to reinforce segmentationalgorithms This assumption represents the basis on whichour approachhas beendeveloped usingCanny edge detectionmethod to detect tumor contours from temperature distribu-tion

The results of the segmentation are illustrated in Figure 7where green and red curves represent segmentation andground truth respectively Each patient is presented in

different column the two first lines present the results ofsegmentation using the level set method on different MRIsequences which are T1 contrast and Flair respectively Thelast line gives the obtained results of segmentation usingthe proposed approach based on Canny edge detection fromtemperature distribution with the addition of Gaussian noiseshown in T1-weighted images At first glance it can beinferred that segmentation has been improved significantlyusing the proposed approach These results demonstratethat temperature provides rich information about tumormargins that can be exploited to have more effective tumorsegmentation in MRI images

International Journal of Biomedical Imaging 9

(Patient 1) (Patient 2) (Patient 3) (Patient 4)

Figure 7 Results of segmentation by level set method in MRI images and the proposed approach The first and second lines provide thesegmentation by level set in T1 contrast and Flair respectively The last line gives the segmentation using the proposed approach showed inT1 Green segmentation Red ground truth

To evaluate the segmentation performance we have usedfive metrics Accuracy Sensitivity Specificity Dice Coeffi-cient and the Jaccard coefficient The obtained results arepresented in Table 2 it presents the calculated segmenta-tion evaluation metrics for level set method in differentMRI sequences and the proposed approach The proposedapproach yields good results compared with level set segmen-tation for the four patients All the metrics were improvedin all cases except Sensitivity which is reduced It can bejustified by the fact that in the segmentation in MRI usinglevel set the number of FN is very weak and sometimesnull as most of the curve of the level set remains outside thetumorous area

The temperature is obtained using the Pennes bioheatequation which can produce errors in calculation comparedto the experimentally measured temperature as the model isisotropic and the used thermal properties donot represent therealistic properties of the patientHowever our interest in thiswork is the way the temperature is diffused and its variationin tumor borders independently of the degree of temperaturerises in the tumorous region Figure 8 shows a one-dimensiontemperature distribution with noise in the tumorous regionsby considering three different values of blood perfusionrate which are 0001 119878minus1 00016 119878minus1 and 0002 119878minus1 for thesame tumor Table 3 furnishes the calculated segmentationevaluation metrics for the proposed approach using Cannymethod applied on three temperature profiles of the sametumor for all patients It can be inferred that the obtainedresults of segmentation are with good accuracy for all cases

In addition there is no significant difference by applying theapproach on three tumor temperature profiles of the sametumor as there is a high variation of temperature in tumorborders in the three cases which proves more the feasibilityand the robustness of the proposed approach

Thus far tumors contours were detected using steady-state thermal analysis where the segmentation was per-formed in the equilibrium state of temperature distributionIn order to study the effect of transient thermal analysis inbrain tumors segmentation cold stress was applied Froman initial temperature distribution at thermal equilibriumobtained using (5) a cold stress temperature 119879119888119900119897119889 wasreducing from each pixel temperature We have consideredthree values for 119879119888119900119897119889 025∘C 05∘C and 1∘C After coolingthe brain (5) was then solved Next thermal images wereobtained at different time steps 5 s 100 s 600 s 1000 s2000 s and 2500 s respectively Table 4 depicts the obtainedresults of segmentation evaluation of the obtained thermalimages at each time step It can be observed that in the threecases for all times steps the approach is still giving acceptableresults which shows the applicability of the approach even intransient temperature distribution

Figures 9 and 10 depict the results of segmentation of levelset in Flair images and the proposed approach in the thermalimages with additional noise of fifty synthetic patients takenfrom BRATS 2012 Training data and BRATS Training datarespectively Tables 5 and 6 report the performance evaluationof the 50 patients In all tested cases the delineation oftumor contours based on temperature distribution showed a

10 International Journal of Biomedical Imaging

Table 2 The calculated segmentation evaluation metrics for level set method and proposed approach

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1T1c 593 191 14609 0 1 0987 09875 08612 07563Flair 592 285 14515 1 09983 09807 09814 08054 06742

Thermal map 556 5 14795 37 09376 09996 09972 09636 09297

Patient 2T1c 1160 287 18241 0 1 09845 09854 08899 08016Flair 1138 369 18159 22 0981 098 09801 08533 07442

Thermal map 1112 37 18491 48 09586 0998 09956 09631 09289

Patient 3T1c 1593 105 18116 28 09827 09942 09932 09599 09229Flair 1598 431 17790 23 09858 09763 09771 08756 07787

Thermal map 1547 7 18214 74 09543 09996 09959 09744 09502

Patient 4T1c 2224 109 17545 204 09159 09938 09844 09342 08766Flair 2428 418 17236 0 1 09763 09791 09207 08531

Thermal map 2375 25 17629 53 09781 09985 09961 09838 09682

Table 3 The calculated segmentation evaluation metrics for the proposed approach by considering different values of blood perfusion rate

Patient No 120596119887 TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 10001 549 3 14797 44 09258 09997 09969 09589 0921100016 556 5 14795 37 09376 09996 09972 09636 092970002 559 5 14795 34 09426 09996 09974 09662 09347

Patient 20001 1099 29 18499 61 09474 09984 09954 09606 0924300016 1112 37 18491 48 09586 0998 09956 09631 092890002 1118 47 18481 42 09637 09974 09954 09617 09262

Patient 30001 1538 1 18220 83 09487 09999 09957 09734 0948200016 1547 7 18214 74 09543 09996 09959 09744 095020002 1558 14 18207 63 09611 09992 09961 09758 09529

Patient 40001 2330 17 17637 98 09596 0999 09942 09759 0952900016 2375 25 17629 53 09781 09985 09961 09838 096820002 2387 31 17623 41 09831 09982 09964 09851 09707

Table 4 The calculated segmentation evaluation metrics for transient thermal analysis in brain tumor contours detection

T119888119900119897119889 (∘C) Time (s) Sensitivity Specificity Accuracy Dice index Jaccard

025

5 09773 09986 0996 09836 09677100 09761 0999 09963 09846 09697600 09794 09985 09962 09842 09691000 09781 09985 09961 09838 0968220000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

05

5 09744 09986 09957 09823 09653100 09703 09993 09958 09826 09659600 0981 09986 09965 09855 097141000 09794 09985 09962 09842 096920000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

10

5 09707 0997 09938 09745 09504100 0953 09994 09938 09741 09495600 09831 09987 09968 09869 097421000 0918 09986 09889 09525 0909420000 09785 09985 09961 0984 0968625000 09781 09985 09961 09838 09682

International Journal of Biomedical Imaging 11

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

20 40 60 80 100 1200Distance (mm)

37

375

38

385

39

395

40

Tem

pera

ture

(∘C)

(a)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(b)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

415

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(c)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

36

37

38

39

40

41

42

43

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(d)

Figure 8 Temperature distribution with noise of brains with realistic tumors of different volumes by considering three values of bloodperfusion rate (a) Tumor with 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with817 cm3 of volume

significant improvement compared to the level set methodexcept Sensitivity and the reason is explained in the previouscase Table 7 presents the percent of the tumor and healthyareas differentiated by segmentation in thermal images onlyin all test cases from the two tables it can be observed thatsegmentation is reinforced using thermal images which leadto more effective segmentation of brain tumors

In Figure 9 and Table 5 patient 4 and patient 5 showfewer values of the used segmentation evaluation metrics(Dice Jaccard etc) using the proposed approach this

assumption is also observed in Figure 10 and Table 6 forpatients 2 and patient 6 This can be explained as thetemperature is calculated using the standard Pennes equationwhich is isotropic model and biological tissues are highlyanisotropic Accordingly the edge detected byCannymethodis smooth and finds difficulties in estimating complex geome-tries In future works we plan to modify the standard Pennesequation to consider anisotropy using MRI Diffusion TensorImaging (DTI) to guide the anisotropy in order to obtainmore accurate and realistic temperature distribution in the

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

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8 International Journal of Biomedical Imaging

GradientTumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(a)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(b)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(c)

Gradient Tumor

80 100 120 140 160 180 200 22060Distance (mm)

0

005

01

015

02

025

(d)

Figure 6 1D representation of temperature absolute gradient on the path passes in the tumor center in the four cases (a) Tumor with 116cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with 817 cm3 of volume

passing across the tumor center It can be inferred that thetemperature gradient always has the maximum value in thetumor contours for all the four patients which proves thattumor thermal profile can provide rich information abouttumor borders that can be used to reinforce segmentationalgorithms This assumption represents the basis on whichour approachhas beendeveloped usingCanny edge detectionmethod to detect tumor contours from temperature distribu-tion

The results of the segmentation are illustrated in Figure 7where green and red curves represent segmentation andground truth respectively Each patient is presented in

different column the two first lines present the results ofsegmentation using the level set method on different MRIsequences which are T1 contrast and Flair respectively Thelast line gives the obtained results of segmentation usingthe proposed approach based on Canny edge detection fromtemperature distribution with the addition of Gaussian noiseshown in T1-weighted images At first glance it can beinferred that segmentation has been improved significantlyusing the proposed approach These results demonstratethat temperature provides rich information about tumormargins that can be exploited to have more effective tumorsegmentation in MRI images

International Journal of Biomedical Imaging 9

(Patient 1) (Patient 2) (Patient 3) (Patient 4)

Figure 7 Results of segmentation by level set method in MRI images and the proposed approach The first and second lines provide thesegmentation by level set in T1 contrast and Flair respectively The last line gives the segmentation using the proposed approach showed inT1 Green segmentation Red ground truth

To evaluate the segmentation performance we have usedfive metrics Accuracy Sensitivity Specificity Dice Coeffi-cient and the Jaccard coefficient The obtained results arepresented in Table 2 it presents the calculated segmenta-tion evaluation metrics for level set method in differentMRI sequences and the proposed approach The proposedapproach yields good results compared with level set segmen-tation for the four patients All the metrics were improvedin all cases except Sensitivity which is reduced It can bejustified by the fact that in the segmentation in MRI usinglevel set the number of FN is very weak and sometimesnull as most of the curve of the level set remains outside thetumorous area

The temperature is obtained using the Pennes bioheatequation which can produce errors in calculation comparedto the experimentally measured temperature as the model isisotropic and the used thermal properties donot represent therealistic properties of the patientHowever our interest in thiswork is the way the temperature is diffused and its variationin tumor borders independently of the degree of temperaturerises in the tumorous region Figure 8 shows a one-dimensiontemperature distribution with noise in the tumorous regionsby considering three different values of blood perfusionrate which are 0001 119878minus1 00016 119878minus1 and 0002 119878minus1 for thesame tumor Table 3 furnishes the calculated segmentationevaluation metrics for the proposed approach using Cannymethod applied on three temperature profiles of the sametumor for all patients It can be inferred that the obtainedresults of segmentation are with good accuracy for all cases

In addition there is no significant difference by applying theapproach on three tumor temperature profiles of the sametumor as there is a high variation of temperature in tumorborders in the three cases which proves more the feasibilityand the robustness of the proposed approach

Thus far tumors contours were detected using steady-state thermal analysis where the segmentation was per-formed in the equilibrium state of temperature distributionIn order to study the effect of transient thermal analysis inbrain tumors segmentation cold stress was applied Froman initial temperature distribution at thermal equilibriumobtained using (5) a cold stress temperature 119879119888119900119897119889 wasreducing from each pixel temperature We have consideredthree values for 119879119888119900119897119889 025∘C 05∘C and 1∘C After coolingthe brain (5) was then solved Next thermal images wereobtained at different time steps 5 s 100 s 600 s 1000 s2000 s and 2500 s respectively Table 4 depicts the obtainedresults of segmentation evaluation of the obtained thermalimages at each time step It can be observed that in the threecases for all times steps the approach is still giving acceptableresults which shows the applicability of the approach even intransient temperature distribution

Figures 9 and 10 depict the results of segmentation of levelset in Flair images and the proposed approach in the thermalimages with additional noise of fifty synthetic patients takenfrom BRATS 2012 Training data and BRATS Training datarespectively Tables 5 and 6 report the performance evaluationof the 50 patients In all tested cases the delineation oftumor contours based on temperature distribution showed a

10 International Journal of Biomedical Imaging

Table 2 The calculated segmentation evaluation metrics for level set method and proposed approach

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1T1c 593 191 14609 0 1 0987 09875 08612 07563Flair 592 285 14515 1 09983 09807 09814 08054 06742

Thermal map 556 5 14795 37 09376 09996 09972 09636 09297

Patient 2T1c 1160 287 18241 0 1 09845 09854 08899 08016Flair 1138 369 18159 22 0981 098 09801 08533 07442

Thermal map 1112 37 18491 48 09586 0998 09956 09631 09289

Patient 3T1c 1593 105 18116 28 09827 09942 09932 09599 09229Flair 1598 431 17790 23 09858 09763 09771 08756 07787

Thermal map 1547 7 18214 74 09543 09996 09959 09744 09502

Patient 4T1c 2224 109 17545 204 09159 09938 09844 09342 08766Flair 2428 418 17236 0 1 09763 09791 09207 08531

Thermal map 2375 25 17629 53 09781 09985 09961 09838 09682

Table 3 The calculated segmentation evaluation metrics for the proposed approach by considering different values of blood perfusion rate

Patient No 120596119887 TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 10001 549 3 14797 44 09258 09997 09969 09589 0921100016 556 5 14795 37 09376 09996 09972 09636 092970002 559 5 14795 34 09426 09996 09974 09662 09347

Patient 20001 1099 29 18499 61 09474 09984 09954 09606 0924300016 1112 37 18491 48 09586 0998 09956 09631 092890002 1118 47 18481 42 09637 09974 09954 09617 09262

Patient 30001 1538 1 18220 83 09487 09999 09957 09734 0948200016 1547 7 18214 74 09543 09996 09959 09744 095020002 1558 14 18207 63 09611 09992 09961 09758 09529

Patient 40001 2330 17 17637 98 09596 0999 09942 09759 0952900016 2375 25 17629 53 09781 09985 09961 09838 096820002 2387 31 17623 41 09831 09982 09964 09851 09707

Table 4 The calculated segmentation evaluation metrics for transient thermal analysis in brain tumor contours detection

T119888119900119897119889 (∘C) Time (s) Sensitivity Specificity Accuracy Dice index Jaccard

025

5 09773 09986 0996 09836 09677100 09761 0999 09963 09846 09697600 09794 09985 09962 09842 09691000 09781 09985 09961 09838 0968220000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

05

5 09744 09986 09957 09823 09653100 09703 09993 09958 09826 09659600 0981 09986 09965 09855 097141000 09794 09985 09962 09842 096920000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

10

5 09707 0997 09938 09745 09504100 0953 09994 09938 09741 09495600 09831 09987 09968 09869 097421000 0918 09986 09889 09525 0909420000 09785 09985 09961 0984 0968625000 09781 09985 09961 09838 09682

International Journal of Biomedical Imaging 11

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

20 40 60 80 100 1200Distance (mm)

37

375

38

385

39

395

40

Tem

pera

ture

(∘C)

(a)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(b)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

415

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(c)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

36

37

38

39

40

41

42

43

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(d)

Figure 8 Temperature distribution with noise of brains with realistic tumors of different volumes by considering three values of bloodperfusion rate (a) Tumor with 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with817 cm3 of volume

significant improvement compared to the level set methodexcept Sensitivity and the reason is explained in the previouscase Table 7 presents the percent of the tumor and healthyareas differentiated by segmentation in thermal images onlyin all test cases from the two tables it can be observed thatsegmentation is reinforced using thermal images which leadto more effective segmentation of brain tumors

In Figure 9 and Table 5 patient 4 and patient 5 showfewer values of the used segmentation evaluation metrics(Dice Jaccard etc) using the proposed approach this

assumption is also observed in Figure 10 and Table 6 forpatients 2 and patient 6 This can be explained as thetemperature is calculated using the standard Pennes equationwhich is isotropic model and biological tissues are highlyanisotropic Accordingly the edge detected byCannymethodis smooth and finds difficulties in estimating complex geome-tries In future works we plan to modify the standard Pennesequation to consider anisotropy using MRI Diffusion TensorImaging (DTI) to guide the anisotropy in order to obtainmore accurate and realistic temperature distribution in the

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

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International Journal of Biomedical Imaging 9

(Patient 1) (Patient 2) (Patient 3) (Patient 4)

Figure 7 Results of segmentation by level set method in MRI images and the proposed approach The first and second lines provide thesegmentation by level set in T1 contrast and Flair respectively The last line gives the segmentation using the proposed approach showed inT1 Green segmentation Red ground truth

To evaluate the segmentation performance we have usedfive metrics Accuracy Sensitivity Specificity Dice Coeffi-cient and the Jaccard coefficient The obtained results arepresented in Table 2 it presents the calculated segmenta-tion evaluation metrics for level set method in differentMRI sequences and the proposed approach The proposedapproach yields good results compared with level set segmen-tation for the four patients All the metrics were improvedin all cases except Sensitivity which is reduced It can bejustified by the fact that in the segmentation in MRI usinglevel set the number of FN is very weak and sometimesnull as most of the curve of the level set remains outside thetumorous area

The temperature is obtained using the Pennes bioheatequation which can produce errors in calculation comparedto the experimentally measured temperature as the model isisotropic and the used thermal properties donot represent therealistic properties of the patientHowever our interest in thiswork is the way the temperature is diffused and its variationin tumor borders independently of the degree of temperaturerises in the tumorous region Figure 8 shows a one-dimensiontemperature distribution with noise in the tumorous regionsby considering three different values of blood perfusionrate which are 0001 119878minus1 00016 119878minus1 and 0002 119878minus1 for thesame tumor Table 3 furnishes the calculated segmentationevaluation metrics for the proposed approach using Cannymethod applied on three temperature profiles of the sametumor for all patients It can be inferred that the obtainedresults of segmentation are with good accuracy for all cases

In addition there is no significant difference by applying theapproach on three tumor temperature profiles of the sametumor as there is a high variation of temperature in tumorborders in the three cases which proves more the feasibilityand the robustness of the proposed approach

Thus far tumors contours were detected using steady-state thermal analysis where the segmentation was per-formed in the equilibrium state of temperature distributionIn order to study the effect of transient thermal analysis inbrain tumors segmentation cold stress was applied Froman initial temperature distribution at thermal equilibriumobtained using (5) a cold stress temperature 119879119888119900119897119889 wasreducing from each pixel temperature We have consideredthree values for 119879119888119900119897119889 025∘C 05∘C and 1∘C After coolingthe brain (5) was then solved Next thermal images wereobtained at different time steps 5 s 100 s 600 s 1000 s2000 s and 2500 s respectively Table 4 depicts the obtainedresults of segmentation evaluation of the obtained thermalimages at each time step It can be observed that in the threecases for all times steps the approach is still giving acceptableresults which shows the applicability of the approach even intransient temperature distribution

Figures 9 and 10 depict the results of segmentation of levelset in Flair images and the proposed approach in the thermalimages with additional noise of fifty synthetic patients takenfrom BRATS 2012 Training data and BRATS Training datarespectively Tables 5 and 6 report the performance evaluationof the 50 patients In all tested cases the delineation oftumor contours based on temperature distribution showed a

10 International Journal of Biomedical Imaging

Table 2 The calculated segmentation evaluation metrics for level set method and proposed approach

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1T1c 593 191 14609 0 1 0987 09875 08612 07563Flair 592 285 14515 1 09983 09807 09814 08054 06742

Thermal map 556 5 14795 37 09376 09996 09972 09636 09297

Patient 2T1c 1160 287 18241 0 1 09845 09854 08899 08016Flair 1138 369 18159 22 0981 098 09801 08533 07442

Thermal map 1112 37 18491 48 09586 0998 09956 09631 09289

Patient 3T1c 1593 105 18116 28 09827 09942 09932 09599 09229Flair 1598 431 17790 23 09858 09763 09771 08756 07787

Thermal map 1547 7 18214 74 09543 09996 09959 09744 09502

Patient 4T1c 2224 109 17545 204 09159 09938 09844 09342 08766Flair 2428 418 17236 0 1 09763 09791 09207 08531

Thermal map 2375 25 17629 53 09781 09985 09961 09838 09682

Table 3 The calculated segmentation evaluation metrics for the proposed approach by considering different values of blood perfusion rate

Patient No 120596119887 TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 10001 549 3 14797 44 09258 09997 09969 09589 0921100016 556 5 14795 37 09376 09996 09972 09636 092970002 559 5 14795 34 09426 09996 09974 09662 09347

Patient 20001 1099 29 18499 61 09474 09984 09954 09606 0924300016 1112 37 18491 48 09586 0998 09956 09631 092890002 1118 47 18481 42 09637 09974 09954 09617 09262

Patient 30001 1538 1 18220 83 09487 09999 09957 09734 0948200016 1547 7 18214 74 09543 09996 09959 09744 095020002 1558 14 18207 63 09611 09992 09961 09758 09529

Patient 40001 2330 17 17637 98 09596 0999 09942 09759 0952900016 2375 25 17629 53 09781 09985 09961 09838 096820002 2387 31 17623 41 09831 09982 09964 09851 09707

Table 4 The calculated segmentation evaluation metrics for transient thermal analysis in brain tumor contours detection

T119888119900119897119889 (∘C) Time (s) Sensitivity Specificity Accuracy Dice index Jaccard

025

5 09773 09986 0996 09836 09677100 09761 0999 09963 09846 09697600 09794 09985 09962 09842 09691000 09781 09985 09961 09838 0968220000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

05

5 09744 09986 09957 09823 09653100 09703 09993 09958 09826 09659600 0981 09986 09965 09855 097141000 09794 09985 09962 09842 096920000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

10

5 09707 0997 09938 09745 09504100 0953 09994 09938 09741 09495600 09831 09987 09968 09869 097421000 0918 09986 09889 09525 0909420000 09785 09985 09961 0984 0968625000 09781 09985 09961 09838 09682

International Journal of Biomedical Imaging 11

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

20 40 60 80 100 1200Distance (mm)

37

375

38

385

39

395

40

Tem

pera

ture

(∘C)

(a)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(b)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

415

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(c)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

36

37

38

39

40

41

42

43

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(d)

Figure 8 Temperature distribution with noise of brains with realistic tumors of different volumes by considering three values of bloodperfusion rate (a) Tumor with 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with817 cm3 of volume

significant improvement compared to the level set methodexcept Sensitivity and the reason is explained in the previouscase Table 7 presents the percent of the tumor and healthyareas differentiated by segmentation in thermal images onlyin all test cases from the two tables it can be observed thatsegmentation is reinforced using thermal images which leadto more effective segmentation of brain tumors

In Figure 9 and Table 5 patient 4 and patient 5 showfewer values of the used segmentation evaluation metrics(Dice Jaccard etc) using the proposed approach this

assumption is also observed in Figure 10 and Table 6 forpatients 2 and patient 6 This can be explained as thetemperature is calculated using the standard Pennes equationwhich is isotropic model and biological tissues are highlyanisotropic Accordingly the edge detected byCannymethodis smooth and finds difficulties in estimating complex geome-tries In future works we plan to modify the standard Pennesequation to consider anisotropy using MRI Diffusion TensorImaging (DTI) to guide the anisotropy in order to obtainmore accurate and realistic temperature distribution in the

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

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Submit your manuscripts atwwwhindawicom

10 International Journal of Biomedical Imaging

Table 2 The calculated segmentation evaluation metrics for level set method and proposed approach

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1T1c 593 191 14609 0 1 0987 09875 08612 07563Flair 592 285 14515 1 09983 09807 09814 08054 06742

Thermal map 556 5 14795 37 09376 09996 09972 09636 09297

Patient 2T1c 1160 287 18241 0 1 09845 09854 08899 08016Flair 1138 369 18159 22 0981 098 09801 08533 07442

Thermal map 1112 37 18491 48 09586 0998 09956 09631 09289

Patient 3T1c 1593 105 18116 28 09827 09942 09932 09599 09229Flair 1598 431 17790 23 09858 09763 09771 08756 07787

Thermal map 1547 7 18214 74 09543 09996 09959 09744 09502

Patient 4T1c 2224 109 17545 204 09159 09938 09844 09342 08766Flair 2428 418 17236 0 1 09763 09791 09207 08531

Thermal map 2375 25 17629 53 09781 09985 09961 09838 09682

Table 3 The calculated segmentation evaluation metrics for the proposed approach by considering different values of blood perfusion rate

Patient No 120596119887 TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 10001 549 3 14797 44 09258 09997 09969 09589 0921100016 556 5 14795 37 09376 09996 09972 09636 092970002 559 5 14795 34 09426 09996 09974 09662 09347

Patient 20001 1099 29 18499 61 09474 09984 09954 09606 0924300016 1112 37 18491 48 09586 0998 09956 09631 092890002 1118 47 18481 42 09637 09974 09954 09617 09262

Patient 30001 1538 1 18220 83 09487 09999 09957 09734 0948200016 1547 7 18214 74 09543 09996 09959 09744 095020002 1558 14 18207 63 09611 09992 09961 09758 09529

Patient 40001 2330 17 17637 98 09596 0999 09942 09759 0952900016 2375 25 17629 53 09781 09985 09961 09838 096820002 2387 31 17623 41 09831 09982 09964 09851 09707

Table 4 The calculated segmentation evaluation metrics for transient thermal analysis in brain tumor contours detection

T119888119900119897119889 (∘C) Time (s) Sensitivity Specificity Accuracy Dice index Jaccard

025

5 09773 09986 0996 09836 09677100 09761 0999 09963 09846 09697600 09794 09985 09962 09842 09691000 09781 09985 09961 09838 0968220000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

05

5 09744 09986 09957 09823 09653100 09703 09993 09958 09826 09659600 0981 09986 09965 09855 097141000 09794 09985 09962 09842 096920000 09781 09985 09961 09838 0968225000 09781 09985 09961 09838 09682

10

5 09707 0997 09938 09745 09504100 0953 09994 09938 09741 09495600 09831 09987 09968 09869 097421000 0918 09986 09889 09525 0909420000 09785 09985 09961 0984 0968625000 09781 09985 09961 09838 09682

International Journal of Biomedical Imaging 11

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

20 40 60 80 100 1200Distance (mm)

37

375

38

385

39

395

40

Tem

pera

ture

(∘C)

(a)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(b)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

415

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(c)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

36

37

38

39

40

41

42

43

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(d)

Figure 8 Temperature distribution with noise of brains with realistic tumors of different volumes by considering three values of bloodperfusion rate (a) Tumor with 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with817 cm3 of volume

significant improvement compared to the level set methodexcept Sensitivity and the reason is explained in the previouscase Table 7 presents the percent of the tumor and healthyareas differentiated by segmentation in thermal images onlyin all test cases from the two tables it can be observed thatsegmentation is reinforced using thermal images which leadto more effective segmentation of brain tumors

In Figure 9 and Table 5 patient 4 and patient 5 showfewer values of the used segmentation evaluation metrics(Dice Jaccard etc) using the proposed approach this

assumption is also observed in Figure 10 and Table 6 forpatients 2 and patient 6 This can be explained as thetemperature is calculated using the standard Pennes equationwhich is isotropic model and biological tissues are highlyanisotropic Accordingly the edge detected byCannymethodis smooth and finds difficulties in estimating complex geome-tries In future works we plan to modify the standard Pennesequation to consider anisotropy using MRI Diffusion TensorImaging (DTI) to guide the anisotropy in order to obtainmore accurate and realistic temperature distribution in the

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

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International Journal of Biomedical Imaging 11

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

20 40 60 80 100 1200Distance (mm)

37

375

38

385

39

395

40

Tem

pera

ture

(∘C)

(a)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(b)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

37

375

38

385

39

395

40

405

41

415

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(c)

w = 0001 [1S]w = 00016 [1S]w = 0002 [1S]

36

37

38

39

40

41

42

43

Tem

pera

ture

(∘C)

20 40 60 80 100 120 1400Distance (mm)

(d)

Figure 8 Temperature distribution with noise of brains with realistic tumors of different volumes by considering three values of bloodperfusion rate (a) Tumor with 116 cm3 of volume (b) Tumor with 274 cm3 of volume (c) Tumor with 511 cm3 of volume (d) Tumor with817 cm3 of volume

significant improvement compared to the level set methodexcept Sensitivity and the reason is explained in the previouscase Table 7 presents the percent of the tumor and healthyareas differentiated by segmentation in thermal images onlyin all test cases from the two tables it can be observed thatsegmentation is reinforced using thermal images which leadto more effective segmentation of brain tumors

In Figure 9 and Table 5 patient 4 and patient 5 showfewer values of the used segmentation evaluation metrics(Dice Jaccard etc) using the proposed approach this

assumption is also observed in Figure 10 and Table 6 forpatients 2 and patient 6 This can be explained as thetemperature is calculated using the standard Pennes equationwhich is isotropic model and biological tissues are highlyanisotropic Accordingly the edge detected byCannymethodis smooth and finds difficulties in estimating complex geome-tries In future works we plan to modify the standard Pennesequation to consider anisotropy using MRI Diffusion TensorImaging (DTI) to guide the anisotropy in order to obtainmore accurate and realistic temperature distribution in the

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

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AerospaceEngineeringHindawiwwwhindawicom Volume 2018

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Submit your manuscripts atwwwhindawicom

12 International Journal of Biomedical Imaging

Table 5 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2012

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 1102 178 17090 66 09434 09896 09867 09 0818Thermal map 1142 18 17250 26 09777 09989 09976 09811 09629

Patient 2 Flair 1067 165 15176 2 09981 09892 09898 09274 08646Thermal map 1038 18 15323 31 0971 09988 0997 09769 09549

Patient 3 Flair 2891 498 14358 4 09986 09664 09717 09201 0852Thermal map 2767 41 14815 128 09557 09972 09904 09703 09424

Patient 4 Flair 1678 342 9386 4 09976 09648 09696 09065 0829Thermal map 1561 44 9684 121 0928 09954 09855 09498 09044

Patient 5 Flair 1810 191 17340 69 09632 09891 09866 09329 08743Thermal map 1803 123 17408 76 09595 09929 09897 09477 09005

Patient 6 Flair 1982 628 16415 1 09994 09631 09669 0863 0759Thermal map 1916 32 17011 67 09662 09981 09947 09748 09508

Table 6 The calculated segmentation evaluation metrics for level set method and proposed approach in BRATS Training data 2013

Patient No Method TP FP TN FN Sensitivity Specificity Accuracy Dice index Jaccard

Patient 1 Flair 2831 371 16731 106 09639 09783 09761 09223 08558Thermal map 2803 71 17031 134 09543 09958 09897 09647 09318

Patient 2 Flair 1378 331 13666 26 09814 09763 09768 08853 07942Thermal map 1307 34 13963 97 09309 09975 09914 09522 09089

Patient 3 Flair 1274 413 17336 10 09922 09767 09777 08576 07507Thermal map 1236 8 17741 48 09626 09995 0997 09778 09566

Patient 4 Flair 1762 560 18243 1 09994 09702 09727 08626 07585Thermal map 1660 33 18770 103 09415 09982 09933 09606 09242

Patient 5 Flair 2015 807 13191 0 1 09423 09496 08331 0714Thermal map 1901 12 13986 114 09434 09991 09921 09679 09378

Patient 6 Flair 1383 644 13522 0 1 09545 09585 08111 06822Thermal map 1271 20 14146 112 091901 09985 09915 09506 09059

Table 7 The percent of tumor and healthy areas differentiated by segmentation in thermal images only

Data set Patient No Reduced false positive rate () Reduced false negative rate ()

Galimzianova et al [50]

Patient 1 016 189Patient 2 103 182Patient 3 012 232Patient 4 0 222

BRATS 2012

Patient 1 513 093Patient 2 018 095Patient 3 0 308Patient 4 0 313Patient 5 127 082Patient 6 0 349

BRATS 2013

Patient 1 224 184Patient 2 178 215Patient 3 077 228Patient 4 0 28Patient 5 0 567Patient 6 0 44

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

International Journal of Biomedical Imaging 13

(a) (b) (c) (d)

Figure 9 Results of segmentation by level set method in Flair MRI images and the proposed approach applied in six patients taken fromBRATS 2012 (a) Flair images (b) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation usingthe proposed approach showed in T1-weighted images (green segmentation red ground truth)

tumorous region Next we will develop a method to applythe proposed approach in clinical realistic MRI images anduse the obtained results to reinforce other recent methods inthe literature to prove more the effectiveness of the proposedapproach

In order to show further the robustness of the proposedapproach we have applied Canny edge detector in obtainedthermal images with additional noise of all 25 patients withhigh-grade tumors taken from BRATS 2012 database and25 patients with low-grade tumors taken from BRATS 2013database Figures 11 12 13 14 and 15 show the obtained

results of Sensitivity Specificity Accuracy Dice index andJaccard respectively for all patients In all the tested casesthe estimation of tumor contours was accurate

In this work we considered temperature distribution forbrain tumor borders delineation Brain tumors modify thenormal temperature due to the variation in heat generationby cells metabolism and blood flow in tumors Temperaturereveals abrupt changes in tumor borders Thus we usedthe Canny edge detection method to locate the edges Theexperiments showed that the proposed approach detectstumor borders with good accuracy and reduces false positive

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

14 International Journal of Biomedical Imaging

(a) (b) (c) (d)

Figure 10 Results of segmentation by level set method in MRI images and the proposed approach applied in six patients taken from BRATS2013 (a) Flair images (c) segmentation by level set in Flair images (c) Temperature distribution with noise (d) the segmentation using theproposed approach showed in T1-weighted images (green segmentation red ground truth)

and false negative of segmentation by level setmethod inMRIimages used in clinical routine To the best of our knowledgewe are the first to incorporated thermal analysis of braintumor in MRI images segmentation

4 Conclusions

Effective and accurate brain tumor segmentation from MRIimages is still a challenging task due to the structuralcomplexity of brain tumors In this paper we proposed a

new approach to enhance brain tumor segmentation based onthe thermal analysis of brain tumors We have presented andinvestigated the effect of tumor on brain temperature distri-bution as well as its size on temperature distribution Next wehave used tumor thermal profile for segmentation to detecttumors contours We calculated the temperature distributionin the brain using Pennes bioheat equation implementedby finite difference method (FDM) The obtained resultswere compared with level set method tested in differentsynthetic MRI sequences of different patients We showed

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

International Journal of Biomedical Imaging 15

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Se

nsiti

vity

(a)

0

01

02

03

04

05

06

07

08

09

1

Sens

itivi

ty

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients(b)

Figure 11 Sensitivity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Spec

ificit

y

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1Sp

ecifi

city

(b)

Figure 12 Specificity in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

(a)

0

01

02

03

04

05

06

07

08

09

1

Accu

racy

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(b)

Figure 13 Accuracy in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (a) 25 with low-grade taken fromBRATS 2013

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

16 International Journal of Biomedical Imaging

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1D

ice i

ndex

(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 22 241 144 7

Patients

0

01

02

03

04

05

06

07

08

09

1

Dic

e ind

ex

(b)

Figure 14 Dice index in thermal images for 50 patients (a) 25 with high-grade taken from BRATS 2012 (b) 25 with low-grade taken fromBRATS 2013

0

01

02

03

04

05

06

07

08

09

1

Jacc

ard

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients(a)

25233 20196 188 9 10 161513 260 12 17112 5 21 224 241 147

Patients

0

01

02

03

04

05

06

07

08

09

1Ja

ccar

d

(b)

Figure 15 Jaccard in thermal images for 50 patients (a) 25with high-grade taken fromBRATS 2012 (b) 25with low-grade taken fromBRATS2013

a significant improvement in segmentation accuracy There-fore the proposed approach can be used as a new indicator toenhance tumors segmentation The present work can be veryuseful towards the creation of a new MRI thermal imagingsequence in future studies which measure the absolute tem-perature distribution as all MR-based temperature-mappingapproaches require a baseline data set

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors of this publication confirm that there are noconflicts of interest associated with this publication and there

has been no significant financial support for this work thatcould have influenced its outcome

Acknowledgments

Thanks are due to theNational Center for Scientific and Tech-nical Research (CNRST-Morocco) (Grant no 13UH22016)

References

[1] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2016rdquoCA A Cancer Journal for Clinicians vol 66 no 1 pp 7ndash30 2016

[2] R L Siegel K D Miller and A Jemal ldquoCancer statistics 2017rdquoCA A Cancer Journal for Clinicians vol 67 no 1 pp 7ndash30 2017

[3] J LiuM Li JWang FWu T Liu andY Pan ldquoA survey ofMRI-based brain tumor segmentation methodsrdquo Tsinghua Scienceand Technology vol 19 no 6 pp 578ndash595 2014

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

International Journal of Biomedical Imaging 17

[4] S Bauer R Wiest L P Nolte and M Reyes ldquoA survey of MRIbased medical image analysis for brain tumor studiesrdquo Physicsin Medicine amp Biology vol 58 no 13 pp 97ndash129 2013

[5] G Helms K Kallenberg and P Dechent ldquoContrast-drivenapproach to intracranial segmentation using a combination ofT2- and T1-weighted 3D MRI data setsrdquo Journal of MagneticResonance Imaging vol 24 no 4 pp 790ndash795 2006

[6] P Gibbs D Buckley S Blackb and A Horsman ldquoTumourdetermination from MR images by morphological segmenta-tionrdquo Physics in Medicine amp Biology vol 41 no 11 pp 2437ndash2446 1996

[7] A Stadlbauer E Moser S Gruber et al ldquoImproved delineationof brain tumors An automatedmethod for segmentation basedon pathologic changes of 1H-MRSI metabolites in gliomasrdquoNeuroImage vol 23 no 2 pp 454ndash461 2004

[8] M R Kaus S K Warfield A Nabavi P M Black F A JoleszandR Kikinis ldquoAutomated segmentation ofMR images of braintumorsrdquo Radiology vol 218 no 2 pp 586ndash591 2001

[9] W Deng W Xiao H Deng and J Liu ldquoMRI brain tumorsegmentation with region growing method based on the gradi-ents and variances along and inside of the boundary curverdquo inProceedings of the 3rd International Conference on BioMedicalEngineering and Informatics BMEI 2010 pp 393ndash396 ChinaOctober 2010

[10] S Taheri S H Ong and V F H Chong ldquoLevel-set segmenta-tion of brain tumors using a threshold-based speed functionrdquoImage and Vision Computing vol 28 no 1 pp 26ndash37 2010

[11] J Sachdeva V Kumar I Gupta N Khandelwal and C KAhuja ldquoA novel content-based active contour model for braintumor segmentationrdquoMagnetic Resonance Imaging vol 30 no5 pp 694ndash715 2012

[12] T Wang I Cheng and A Basu ldquoFluid vector flow andapplications in brain tumor segmentationrdquo IEEE Transactionson Biomedical Engineering vol 56 no 3 pp 781ndash789 2009

[13] A M Hasan F Meziane R Aspin and H A Jalab ldquoSegmen-tation of brain tumors in MRI images using three-dimensionalactive contour without edgerdquo Symmetry vol 8 no 11 Art 13221 pages 2016

[14] L M Fletcher-Heath L O Hall D B Goldgof and F RMurtagh ldquoAutomatic segmentation of non-enhancing braintumors in magnetic resonance imagesrdquo Artificial Intelligence inMedicine vol 21 no 1-3 pp 43ndash63 2001

[15] A Veloz S Chabert R Salas A Orellana and J Vielma ldquoFuzzyspatial growing for glioblastomamultiforme segmentation onbrain magnetic resonance imagingrdquo LNCS vol 4756 pp 861ndash870 2008

[16] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with Deep Neural Networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[17] S Pereira A Pinto V Alves and C A Silva ldquoBrain TumorSegmentation Using Convolutional Neural Networks in MRIImagesrdquo IEEE Transactions on Medical Imaging vol 35 no 5pp 1240ndash1251 2016

[18] A Isin C Direkoglu and M Sah ldquoReview of mri-basedbrain tumor image segmentation using deep learningmethodsrdquoProcedia Computer Science vol 102 pp 317ndash324 2016

[19] MKass AWitkin andD Terzopoulos ldquoSnakes active contourmodelsrdquo International Journal of Computer Vision vol 1 no 4pp 321ndash331 1988

[20] S Osher and J A Sethian ldquoFronts propagating with curvature-dependent speed algorithms based onHamilton-Jacobi formu-lationsrdquo Journal of Computational Physics vol 79 no 1 pp 12ndash49 1988

[21] N Gordillo E Montseny and P Sobrevilla ldquoState of the artsurvey onMRI brain tumor segmentationrdquoMagnetic ResonanceImaging vol 31 no 8 pp 1426ndash1438 2013

[22] K Das and S C Mishra ldquoEstimation of tumor characteristicsin a breast tissue with known skin surface temperaturerdquo Journalof Thermal Biology vol 38 no 6 pp 311ndash317 2013

[23] G Shi F Han C Liang L Wang and K Li ldquoA novel method ofthermal tomography tumor diagnosis and its clinical practicerdquoAppliedThermal Engineering vol 73 no 1 pp 408ndash415 2014

[24] R Hatwar and C Herman ldquoInverse method for quantitativecharacterisation of breast tumours from surface temperaturedatardquo International Journal of Hyperthermia pp 1ndash17 2017

[25] Z-J Fu Q Xi L Ling and C-Y Cao ldquoNumerical investigationon the effect of tumor on the thermal behavior inside the skintissuerdquo International Journal of Heat andMass Transfer vol 108pp 1154ndash1163 2017

[26] A Ramırez-Torres et al ldquoThe role of malignant tissue on thethermal distribution of cancerous breastrdquo Journal of TheoreticalBiology vol 426 pp 152ndash161 2017

[27] R Jayasundar and V P Singh ldquoIn vivo temperature mea-surements in brain tumors using proton MR spectroscopyrdquoNeurology India vol 50 no 4 pp 436ndash439 2002

[28] A M Gorbach J D Heiss L Kopylev and E H OldfieldldquoIntraoperative infrared imaging of brain tumorsrdquo Journal ofNeurosurgery vol 101 no 6 pp 960ndash969 2004

[29] B Kateb V Yamamoto C Yu W Grundfest and J P GruenldquoInfrared thermal imaging a review of the literature and casereportrdquoNeuroImage vol 47 no 2 pp T154ndashT162 2009

[30] M Sadeghi-Goughari A Mojra and S Sadeghi ldquoParame-ter estimation of brain tumors using intraoperative thermalimaging based on artificial tactile sensing in conjunction withartificial neural networkrdquo Journal of Physics D Applied Physicsvol 49 no 7 2016

[31] K Das R Singh and S C Mishra ldquoNumerical analysis fordetermination of the presence of a tumor and estimation of itssize and location in a tissuerdquo Journal ofThermal Biology vol 38no 1 pp 32ndash40 2013

[32] A Bousselham O Bouattane M Youssfi and A Raihanildquo3D brain tumor localization and parameter estimation usingthermographic approach on GPUrdquo Journal of Thermal Biologyvol 71 pp 52ndash61 2018

[33] R Hatwar and C Herman ldquoInverse method for quantitativecharacterization of breast tumors from surface temperaturedatardquo International Journal of Hyperthermia vol 33 no 7 pp741ndash757 2017

[34] H H Pennes ldquoAnalysis of tissue and arterial blood tem-peratures in the resting human forearmrdquo Journal of AppliedPhysiology vol 1 no 2 pp 93ndash122 1948

[35] E H Wissler ldquoPennesrsquo 1948 paper revisitedrdquo Journal of AppliedPhysiology vol 85 no 1 pp 35ndash41 1998

[36] W Wulff ldquoThe energy conservation equation for living tissuerdquoIEEE Transactions on Biomedical Engineering vol 21 no 6 pp494-495 1974

[37] MM Chen and K R Holmes ldquoMicrovascular contributions intissue heat transferrdquoAnnals of theNewYorkAcademy of Sciencesvol 335 no 1 pp 137ndash150 1980

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

18 International Journal of Biomedical Imaging

[38] H G Klinger ldquoHeat transfer in perfused biological tissuemdashIGeneral theoryrdquo Bulletin of Mathematical Biology vol 36 pp403ndash415 1974

[39] MM Elwassif Q Kong M Vazquez andM Bikson ldquoBio-heattransfer model of deep brain stimulation-induced temperaturechangesrdquo Journal of Neural Engineering vol 3 no 4 article no008 2006

[40] H Zhang ldquoLattice Boltzmann method for solving the bioheatequationrdquo Physics in Medicine and Biology vol 53 no 3 ppN15ndashN23 2008

[41] J P Agnelli A A Barrea and C V Turner ldquoTumor locationand parameter estimation by thermographyrdquoMathematical andComputer Modelling vol 53 no 7-8 pp 1527ndash1534 2011

[42] J Wang and O Fujiwara ldquoFDTD computation of temperaturerise in the human head for portable telephonesrdquo IEEE Trans-actions on Microwave Theory and Techniques vol 47 no 8 pp1528ndash1534 1999

[43] M Menezes de Oliveira P Wen and T Ahfock ldquoHeat transferdue to electroconvulsive therapy Influence of anisotropic ther-mal and electrical skull conductivityrdquo Computer Methods andPrograms in Biomedicine vol 133 pp 71ndash81 2016

[44] P Vaupel F Kallinowski and P Okunieff ldquoBlood flow oxy-gen and nutrient supply and metabolic microenvironment ofhuman tumors a reviewrdquo Cancer Research vol 49 no 23 pp6449ndash6465 1989

[45] T F Chan and L A Vese ldquoActive contourswithout edgesrdquo IEEETransactions on Image Processing vol 10 no 2 pp 266ndash2772001

[46] DMumford and J Shah ldquoOptimal approximations by piecewisesmooth functions and associated variational problemsrdquo Com-munications on Pure and Applied Mathematics vol 42 no 5pp 577ndash685 1989

[47] R Crandall Image Segmentation Using the Chan-Vese Algo-rithm Project Report TechnionndashIsrael Institute of TechnologyHaifa Israel 2009

[48] J Canny ldquoA computational approach to edge detectionrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol8 no 6 pp 679ndash698 1986

[49] H Menze A Jakab S Bauer et al et al ldquoThe multimodalbrain tumor image segmentation benchmark (brats)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

[50] A Galimzianova F Pernus B Likar and Z Spiclin ldquoRobustestimation of unbalanced mixture models on samples withoutliersrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 37 no 11 pp 2273ndash2285 2015

[51] M Prastawa E Bullitt and G Gerig ldquoSimulation of braintumors in MR images for evaluation of segmentation efficacyrdquoMedical Image Analysis vol 13 no 2 pp 297ndash311 2009

[52] A Ahlgren R Wirestam F Stahlberg and L Knutsson ldquoAuto-matic brain segmentation using fractional signal modeling of amultiple flip angle spoiled gradient-recalled echo acquisitionrdquoMagnetic Resonance Materials in Physics Biology and Medicinevol 27 no 6 pp 551ndash565 2014

[53] N Nabizadeh N John and C Wright ldquoHistogram-basedgravitational optimization algorithm on single MR modalityfor automatic brain lesion detection and segmentationrdquo ExpertSystems with Applications vol 41 no 17 pp 7820ndash7836 2014

[54] V G Kanas E I Zacharaki C Davatzikos K N Sgarbas andV Megalooikonomou ldquoA low cost approach for brain tumorsegmentation based on intensity modeling and 3D Random

Walkerrdquo Biomedical Signal Processing and Control vol 22 pp19ndash30 2015

[55] B Aubert-Broche M Griffin G B Pike A C Evans and DL Collins ldquoTwenty new digital brain phantoms for creationof validation image data basesrdquo IEEE Transactions on MedicalImaging vol 25 no 11 pp 1410ndash1416 2006

[56] M Kistler S Bonaretti M Pfahrer R Niklaus and P BuchlerldquoThe virtual skeleton database An open access repositoryfor biomedical research and collaborationrdquo Journal of MedicalInternet Research vol 15 no 11 p e245 2013

[57] B H Menze A Jakab S Bauer et al ldquoThe multimodalbrain tumor image segmentation benchmark (BRATS)rdquo IEEETransactions on Medical Imaging vol 34 no 10 pp 1993ndash20242015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

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