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Computer Methods and Programs in Biomedicine 155 (2018) 153–164
Contents lists available at ScienceDirect
Computer Methods and Programs in Biomedicine
journal homepage: www.elsevier.com/locate/cmpb
Spatiotemporal features of DCE-MRI for breast cancer diagnosis
Masood Banaie
a , Hamid Soltanian-Zadeh
a , b , ∗, Hamid-Reza Saligheh-Rad
c , Masoumeh Gity
d
a Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran,
Tehran, Iran b Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA c Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science, Tehran, Iran d Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Medical Imaging Center, Imam Khomeini Hospital, Tehran University of Medical
Sciences, Tehran, Iran
a r t i c l e i n f o
Article history:
Received 25 April 2017
Revised 9 November 2017
Accepted 12 December 2017
Keywords:
Computer aided diagnosis
Breast cancer
DCE-MRI
Feature fusion
a b s t r a c t
Background and Objective: Breast cancer is a major cause of mortality among women if not treated in
early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on
specific sets of features that can be called spatial features or temporal features. However, limited set
of features carry limited information, requiring complex classification methods to diagnose the disease.
For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imag-
ing techniques that provides temporal information about the kinetics of the contrast agent in suspicious
lesions along with acceptable spatial resolution.
Methods: We have extracted and studied a comprehensive set of features from spatiotemporal space to
obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion
technique to remove common information and extract a feature set with maximum information to be
used by a simple classification method. We have also implemented conventional feature selection and
classification methods and compared them with our proposed approach.
Results: Experimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven
patients illustrate that the proposed method outperforms alternative methods. The proposed method
achieves a classification accuracy of 99% without missing any of the malignant cases.
Conclusions: The proposed method may help physicians determine the likelihood of malignancy in breast
cancer using DCE-MRI without biopsy.
© 2017 Elsevier B.V. All rights reserved.
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. Introduction
Breast cancer is one of the diseases that are widespread in the
orld, especially in the developed countries. Breast cancer is the
ost prevalent cancer (22% of all new cases) as well as the lead-
ng cause of cancer deaths in women worldwide (14% of all cancer
eaths). On the basis of the current incidence, it is predicted that
ne in eight women in the United States will develop breast can-
er in her lifetime [1] . Consequently, development of approaches to
ffectively diagnose the disease in its early stages has been a hot
opic in recent years.
A number of studies have focused on mammography for pri-
ary assessment of the disease [2–5] . Physicians usually use mam-
ography for screening purposes to detect suspicious lesions in
he breast. Because of its ease of use and low cost, some research
∗ Corresponding author at: Radiology Image Analysis Lab., Henry Ford Health Sys-
em, One Ford Place, 2F, Detroit, MI 48202, USA.
E-mail addresses: [email protected] , [email protected] (H. Soltanian-Zadeh).
o
m
H
ttps://doi.org/10.1016/j.cmpb.2017.12.015
169-2607/© 2017 Elsevier B.V. All rights reserved.
tudies have focused on these images to reach maximum detection
ate for the breast cancer in the early stages.
Segmentation of the lesions and suspected areas in the mam-
ography images has been challenging because of the low quality
f these images [2–6] . Mammography images present limited in-
ormation about the tissue and thus are insufficient for advanced
nalyses and diagnosis.
Some of the studies have focused on ultrasound imaging, which
s dynamic and low cost [7–11] . Although ultrasound images can
nclude some dynamic information about the tissue in 3-D, in
ome cases, it is not used to characterize the passage of a contrast
gent in lesions, which is essential for determining the temporal
eatures that are based on the passage of contrast media. There-
ore, in most of the suspected cases, physicians use Magnetic Res-
nance Imaging (MRI) data for more detailed analysis.
Different studies have shown that MRI is more valuable than
ther imaging techniques for analyzing breast cancer and analysis
ethods generate superior results when using this data [12–15] .
owever, conventional MRI of the breast is less informatic in com-
154 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164
Table 1
Parameters of the pulse sequence
used for DCR-MRI data acquisition.
Repetition Time 9 ms
Echo Time 4.2 ms
Flip Angle 30 °Field of View (FOV) 32–40 cm
Matrix Size 512 × 512
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parison with specialized dynamic imaging methods such as Dy-
namic Contrast Enhanced MRI (DCE-MRI) [16] .
Nowadays, Dynamic Contrast Enhanced MRI (DCE-MRI) plays an
important role in the diagnosis of breast cancer and is analyzed
by novel methods to help physicians diagnose breast cancer [17–
20] . Because of the rich information supplied by DCE-MRI, a major
portion of studies in this field has focused on predicting response
of the disease to treatments such as neoadjuvant chemotherapy
[21–36] .
Depending on the required precision and the allowed imaging
time, DCE-MRI is acquired in a number of phases. It includes a pre-
contrast set of images that allows tracking of the changes in the
wash-in and washout behavior of the contrast agent in the breast
tissue during the time that the images are acquired.
Different studies have used different parts of the information
that exist in the acquired data. They have extracted different fea-
tures from the data and used various classification techniques to
provide physicians with the best primary diagnosis results. Dif-
ferent CAD systems have been developed and evaluated based on
these studies to ease the diagnosis process for physicians [37–39] .
Some of the studies use semi-quantitative methods to ana-
lyze the DCE-MRI information. However, the methods mostly lack
reproductively [24,40] and are thus inappropriate for computer-
ized diagnosis systems. Recent studies have mainly focused on the
quantification of the features described in the reference medical
texts, which are based on the physicians’ experiences.
The imaging features can be divided into two main groups: spa-
tial features and temporal features. Spatial features have been de-
veloped for static (single) images. The approaches for extracting
these features have evolved over the years. These features include
different mathematical descriptors for the borders and shapes of
the suspected lesions. Some of these descriptions consider Breast
Imaging Reporting and Data System (BI-RADS). BI-RADS was origi-
nally developed for mammography to standardize the reporting of
the severity of findings. American College of Radiology (ACR) pub-
lished and trademarked BI-RADS based on the requests and opin-
ions of the researches working in the field. It may be used by doc-
tors to specify a patient’s risk of developing breast cancer [41] . The
segmentation quality affects the quality of the spatial features.
Temporal features focus on dynamic behavior of the contrast
agent in the lesions during consecutive recordings. It has been
shown that for accurate modelling of the kinetic behavior of the
lesions, the time difference between consecutive recordings should
be 16 sec or less [42] . This acquisition time would make the spa-
tial resolution of the images very low. Therefore, some studies –
including current study – have used the temporal features of the
images acquired in higher acquisition times for the diagnosis of
the breast cancer [25,43–45] .
Using the information available in each feature space, an appro-
priate feature fusion method that employs the information from
both of the feature spaces can help with the classification process
and improve diagnosis accuracy. In this study, we use the informa-
tion present in the dynamic images to diagnose the breast cancer
in an effective manner. We use images of the cases that were ini-
tially suspected to be malignant. However, according to the biopsy
results, some of these cases were eventually diagnosed as malig-
nant or benign and the remaining cases were diagnosed as benign
based on timely follow-ups. About two thirds of the cases were
reported to have marginal diagnosis when the experts evaluated
their imaging data. The database includes some false positive cases.
The main contributions of this study are as follows:
• Extracting a comprehensive set of features including the fea-
tures proposed in the previous studies in the spatial or tempo-
ral spaces, from a balanced dataset of patients (equal number
of cases in each group).
• Proposing a fusion method to fulfill both reduction and priori-
tization of the features in a nonlinear approach to avoid infor-
mation repetition in the mixture of the two spaces along with
optimal use of information present in each space. • Achieving an accuracy rate higher than those of the existing
methods for the separation of benign and malignant breast can-
cers using DCE-MRI.
Section 2 discusses characteristics of the database, methods of
eature extraction and fusion along with classification methods.
he results of the proposed method in comparison with previous
tudies are discussed in Section 3 . A discussion of the results and
he proposed method is provided in Section 4 . Section 5 concludes
he study.
. Materials and methods
.1. Database
The dataset included the DCE-MRI data of 26 biopsy or short-
erm follow-up proven patients. The wide range of age from 30
o 69 provided a balanced dataset for our research. The average
ge of the two groups was similar (45.94 for benign and 45.54 for
alignant cases).
Some patients had more than one lesion eligible for the analy-
is. Then, the dataset included 16 benign lesions and 16 malignant
esions. Two sample images from the dataset are shown in Fig. 1 .
A large portion of the previous studies used physician’s reports,
hich were based on qualitative assessment of the images. When
e compared the reports and final diagnosis of the patients, we
ound diagnosis differences for patients with BIRADS 3 and 4 clas-
ification. Although the final diagnosis based on biopsy did not al-
ays match the initial report, none of the patients were excluded
rom the study but grouping of the patients was done based on the
nal diagnosis.
Based on the pathology reports of the patients (excluding those
iagnosed based on a 6 months follow-up), the masses included in
he study were diagnosed as:
• Benign masses, including fibroadenoma, non-proliferative fi-
brocystic changes, adenosis, intraductal papillomas, and duct
ectasis. • Malignant masses, including invasive ductal carcinoma, and in-
vasive lobular carcinoma.
.2. Data acquisition
The images were acquired using a 1.5 Tesla MRl system that
ad a dedicated bipolar phased-array breast coil (GE Healthcare,
ilwaukee, WI, USA). A T1-weighted 3D spoiled gradient-recalled
cho pulse sequence was used to acquire the DCE-MRI of the
reast. The parameters of the pulse sequence are listed in Table 1 .
Six series of images, one pre-contrast and five post-contrast,
ere acquired. Each dynamic acquisition series had a temporal res-
lution of about 80 s (varies slightly depending on the patient’s
reast size) and an average of 64 axial slices. Before the injection
f the contrast agent, the first (pre-contrast) series was acquired.
M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 155
Fig. 1. A slice of DCE-MRI from two different patients showing suspected lesions.
Fig. 2. Sample images of dynamic changes of the contrast for three benign lesions.
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hen, 0.2 mmol/Kg of Gd-DTPA was injected manually and intra-
enously following by 20 cc normal saline. Next, five post-contrast
eries were acquired. Figs. 2 and 3 show sample images of the
reast with a benign or malignant lesion, acquired in consequent
cquisition times.
.3. Feature extraction
We extracted the features from the suspected lesions in the im-
ges, then, a specialist marked the lesions on the post-contrast im-
ges. We used a technique based on an initial point selection in-
ide the lesion. Considering a specific lesion, all the pixel intensi-
ies inside the lesion borders belong to a specific range of inten-
ities. Our method finds the pixels of the image that are brighter
han the threshold computed using the initial point. Then, for le-
ion segmentation, we extracted the connected component that in-
luded the initial point. Finally, the border of the segmented lesion
as shown (see Fig. 4 ). Extracted borders were double-checked by
physician to validate the segmentation results.
.3.1. Spatial features
The extracted lesions are processed using different methods to
uantify their shapes and textures. These features represent the
haracteristics usually used by physicians to determine the nature
f different lesions in the breast images. These characteristics are
ostly discussed in Breast Imaging Reporting And Data System (BI-
ADS) which is a widely accepted risk assessment and quality as-
urance tool in mammography, ultrasound, and MRI. The system
as been designed to make the reporting of mammograms stan-
ard and comprehensible to the non-radiologist reading the report
41] . A system for automatic risk assessment based on the com-
uterizing the described features of the lesions may help physi-
ians by increasing the precision for the diagnosis process. In this
tudy, different feature sets that can describe different spatial as-
156 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164
Fig. 3. Sample images of dynamic changes of the contrast for three malignant lesions.
Fig. 4. Segmentation results for the sample images of Fig. 1 . The border of the segmented region is shown using a thick white line.
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pects of the lesion are considered. Then, a set of important fea-
tures for diagnosis of cancer based on BIRADS that includes all the
information about the lesion is selected. The features extracted in
this section quantify the shape of the segmented lesion. These fea-
tures consider the smoothness of the border and the regularity of
the shape. Based on the BIRADS descriptors for the severity of the
lesion, it can be noted that irregular masses are more probable
to be cancerous [41,46] . In addition, the system includes descrip-
tors based on the dynamic behavior of the enhancement (temporal
features).
Texture features are extracted based on the Gray-Level Co-
Occurrence Matrix (GLCM) [47–49] . These features include autocor-
relation, sum of squares, sum average and variance, difference vari-
ance and entropy, information measure of correlation, inverse dif-
ference, inverse difference normalized, inverse difference moment
normalized, uniformity, energy, and angular second moment.
Shape features including Fourier factor for farthest point and
radial length are added to the texture feature space to complete
ihe spatial features. Farthest point distance is calculated as:
P D ( u ) =
√
( x ( u ) − x c ) 2 + ( y ( u ) − y c )
2
+
√ (x f p ( u ) − x c
)2 +
(y f p ( u ) − y c
)2
here ( x fp (u), y fp ( u )) is the farthest point and ( x c , y c ) is the center
oint as shown on a sample lesion in Fig. 5 . This feature is related
o the shape of the lesion and is a measure of the irregularity of
he border, which usually increases in cancerous lesions.
Consequently, the spatial feature set include 51 features (3
hape features and 48 texture features) that represent the spatial
haracteristics of the lesion.
.3.2. Temporal features
Temporal Features can be calculated using model-based and
odel-free methods. Both methods have been used for analyz-
ng DCE-MRI of breast cancer [50] . Model-free methods consider
M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 157
Fig. 5. An illustration of the center point and farthest point. Here, (x c ,y c ) is the
coordinates of the center point and (x fp ,y fp ) is the coordinates of the farthest point
for a sample lesion.
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escriptions that are mentioned in BIRADS [46] . They are semi-
uantitative and subjective and thus depend on the quantification
ethod used. The parameters of the models vary based on the as-
umptions made. Previous studies have shown that there is a good
orrelation between model-based and model-free methods [51] . In
his study, we include all features that may extract dynamic in-
ormation of the data. A set of features are extracted by fitting
he standard and modified Tofts models [52,53] to the dynamic
hanges in the lesion. The Tofts model is a compartmental model
hat considers blood vessels and the tissue as two connected com-
artments. The formulation of this model is:
t ( t ) = K
trans
∫ C p ( τ ) e −k ep ( t−τ ) dτ
here C t is the tracer concentration in the tissue, K
trans is a con-
tant value called transfer constant, C p is the tracer concentration
n the arterial blood plasma, and k ep is the rate constant. The equa-
ion is based on a general model for transfer between two com-
artments: one with high density of a matter and the other with
ow density of the same matter. Most of the studies using this fea-
ure extraction method consider K
trans as a feature, which plays an
mportant role in the classification.
For a better understanding of the idea behind the Tofts model,
graphical illustration is shown in Fig. 6 .
ig. 6. An illustration of how the Gd-DTPA contrast gets in and out of a breast lesion. The
esion is considered in the models.
The Modified-Tofts (M-Tofts) tracer kinetic model (standard
ofts model plus plasma volume, vp) is often used to analyze DCE-
RI data. It represents a particular case of the two-compartment
xchange model with two simplifying assumptions generally: (1)
egligible plasma mean transit time (MTT); and (2) plasma flow
Fp) much greater than microvascular permeability-surface area
PS). The two-compartment exchange model provides separate es-
imates of PS and Fp, while the M-Tofts model only provides an
stimate of the transfer constant, K
trans . K
trans reflects PS if the sim-
lifying assumptions are met and it may be sensitive to Fp if they
re not met.
In addition, to enrich the feature set, additional features are ex-
racted by fitting the data to the Brix model [54] . The features ex-
racted from the model parameters can be effective in the classi-
cation process. In the model, the signal enhancement is assumed
o be proportional to the CA concentration in the tissue. Then, the
lasma concentration is fitted with single exponential decay char-
cterized by a rate constant k el . The Brix model formula is:
( t.A. k el . k ep ) =
A
k el − k ep
(1 − exp ( −k ep t )
k ep − 1 − exp ( −k el t )
k el
)
here A is the initial slope of the curve.
To optimize the information extracted from the dynamic im-
ges, we have included the parameters extracted from the Adia-
atic approximation of the Tissue Homogeneity (ATH) model in the
eature set. This approximation assumes that the tracer concentra-
ion in parenchymal tissue changes slowly relative to that in cap-
llaries. Based on these assumptions, a time-domain, closed-form
olution of the tissue homogeneity model is derived. The form of
he solution, which is usually called the adiabatic solution, is sim-
lar to those of two-compartment models [55,56] .
The model is more complex than the previous models. It as-
umes that the concentration of CA in the tissue ( C t ( t )) is equal to
he convolution of the arterial input function ( C p ( t )) with the fol-
owing model-based impulse response function ( H ( t )):
( t ) =
⎧ ⎨
⎩
F P t ≤ T c
E · F P exp
(−E · F P
v e ( t − T c )
)t > T c
The model considers different parameters including: plasma
ow rate ( F p ), extraction fraction through first-passage ( E ), mean
contrast agent is injected to the veins. The exchange of CA between veins and the
158 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164
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capillary transit time ( T c ), and interstitial volume fraction ( v e ). The
parameters described above are related as follows:
K
trans = E · F
v p = T c · F
The model parameters are selected as features and their values
evaluated for classification.
Some recent studies showed that a centripetal/centrifugal en-
hancement pattern was valuable in the diagnosis process [57] .
Therefore, this feature was also included in the pool of features.
In this study, the feature for type of enhancement was consid-
ered in 5 levels. A large portion of the patients could not be val-
ued as centrifugal or centripetal by the physicians. Then, a state
named none is considered for these patients. Moreover, to make
this feature more informative for the classification process, each
enhancement group was also divided into two sets based on their
enhancement level.
Finally, the feature set includes the following 22 features ex-
tracted from the temporal (dynamic) information of the images.
• k Trans using Modified Tofts calculating AIF • kep using Modified Tofts calculating AIF • k Trans using Modified Tofts • kep using Modified Tofts • vp using Modified Tofts • A using Brix Model • kep using Brix Model • kel using Brix Model • Tc using ATH Model • E using ATH Model • ve using ATH Model • tc using Schabel Model and Parker AIF • alfa using Schabel Model and Parker AIF • E using Schabel Model and Parker AIF • F using Schabel Model and Parker AIF • tc using Schabel Model and Weinmann AIF • alfa using Schabel Model and Weinmann AIF • kep using Schabel Model and Weinmann AIF • F using Schabel Model and Weinmann AIF • Cenrtifugal Features • k Trans using Standard Tofts • kep using Standard Tofts
Fig. 7 shows a tree representation of the feature types used as
the primary features.
2.4. Feature fusion
Various studies have focused on using features from different
domains to improve the classification process [58–62] . Most of the
studies on breast cancer diagnosis use computational approaches
and focus on different classification methods to reach the best di-
agnosis rate using spatial or temporal features. These studies were
reviewed in previous sections of this paper. Since the spatial and
temporal features include different information, using both feature
spaces can increase accuracy of the diagnosis process. In this study,
we applied different techniques to fuse the mentioned features. In
addition, since feature reduction is another target for the process
and different studies in this subject have used SVM for the clas-
sification process, Kernel Principal Component Analysis (KPCA) is
used for this purpose. KPCA uses kernel methods to extend Prin-
cipal Component Analysis (PCA). This extension allows the linear
operations of PCA be done in a reproducing kernel Hilbert space
with a non-linear mapping. The principal components may be ef-
ficiently computed in high dimensional feature spaces that are re-
lated to the input space by some nonlinear maps [63] . Some previ-
us studies have used this method to improve discriminating abil-
ty of the features [64–66] . Fig. 8 provides a schematic comparison
f the linear PCA and Kernel PCA methods.
This method would find a suitable kernel function for the
ataset. Then, it would decrease the dimension of the feature set
hich would decrease the classifier complexity. Decreasing the
lassification complexity is a main advantage of this method. Be-
ause the number of patients in the dataset is limited, in this stage
f the study, we decreased the number of features to four. The four
eatures in 2-D spaces are shown in Fig. 9 . These features are the
esult of a mapping that combines the information of the input set
f features and outputs new features with effective discriminative
alue. However, if the size and variety of the information in the
ataset increase, a higher dimensional feature space is needed to
nclude all discriminative information.
.5. Classification
In this stage of the study, we applied different classification
ethods. Previous studies have used complex classification meth-
ds but this study simplifies the classification stage. We use a sim-
le feed-forward neural network with three neurons in the hidden
ayer. Moreover, the output of the neural network is rounded to
he nearest integer to achieve results that are comparable with the
arget values. The network classifies the input data, which includes
he features of the two groups (malignant and benign).
We have also developed a reference/conventional method based
n the techniques widely used in the literature. To this end, we re-
uced the input feature set to four features using a straightforward
eature reduction method. This made it possible to obtain a gener-
lizable classification by preventing over-fitting. We then used a
upport Vector Machine (SVM) to classify the reduced feature set.
To avoid overfitting, we separated the test and train sets before
he fusion stage. This means that the trained classifier was evalu-
ted on the test data, which was not used in the training of either
he fusion or the classification methods.
. Results
Features from the spatial and temporal spaces were selected
onsidering those used in the previous studies. In this section, we
rst present a primary statistical analysis to determine the effec-
iveness of the individual features for benign and malignant lesions
lassification. Then, we present the classification results using all of
he spatial and temporal features. Next, we describe the classifica-
ion results of the spatial features alone or the temporal features
lone.
.1. Evaluation of effectiveness of individual features
To evaluate the effectiveness of a feature to differentiate the
wo groups, we used two approaches: (1) class discrimination and
2) correlation with the class labels.
In the first approach, we applied a two-sample t -test. This test
s used to statistically evaluate the difference between two popu-
ation means when standard deviations are unknown and samples
re drawn independently. When the sample size is limited, this test
orks best if the data are drawn from normal or close to normal
istributions. As expected, the confidence in the results increases
s the sample sizes increase [67,68] .
In our test, the null-hypothesis assumes that the data of the
wo groups are from independent random samples of normal dis-
ributions with equal means and unknown variances. Considering
p -value of .01, the null hypothesis was rejected in 16 of the spa-
ial features and 7 of the temporal features, i.e. , these features may
iscriminate the two groups. The results illustrate that there are
M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 159
Fig. 7. Groups of features used in the study.
Fig. 8. (a) Linear PCA computes the projections of the features onto the eigenvectors of the covariance matrix. Linear PCA uses the eigenvectors of the covariance matrix to
generate an orthogonal transformation of the coordinate system. (b) kernel PCA maps the input feature space by a nonlinear transformation M. This transforms the contour
lines of constant projections onto the principal eigenvector to curves in the input feature space. Green crosses show the data samples. (For interpretation of the references
to color in this figure legend, the reader is referred to the web version of this article.)
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everal potentially discriminating features, which need to be ana-
yzed using feature selection, feature combination, and classifica-
ion methods.
Table 2 shows the list of the top-ten features selected by the
-test along with their associated p-values, their ranks in the pro-
osed feature fusion approach, and a list of other studies that have
sed the same features for the classification of similar datasets.
In the second approach, we estimated the correlation of each
eature with the class labels ( −1 for the benign and + 1 for the ma-
ignant cases). Interestingly, nine out of the top-ten features were
160 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164
Fig. 9. The four resulting features, shown in 2-D feature spaces.
Table 2
Top-ten features selected by the t -test, their associated p -values, their rank in the proposed feature fusion approach, and a list of other studies that have
used the same features.
Feature Name p -value Rank in 1st Rank in 2nd Rank in 3rd Rank in 4th Other studies that used
fused feature fused feature fused feature fused feature the feature
Correlation 2 from GLCM 2 p < 10 −7 68 48 9 70 [47–49]
Cluster Prominence from GLCM 1 p < 10 −6 67 11 16 53 [47–49]
Cenrtifugal Features p < 10 −6 9 3 4 10 [24,33,40,57]
Fourier Factor of Radial length p < 10 −5 18 50 50 18 [57,64]
Correlation 1 from GLCM 1 p < 10 −4 70 9 13 2 [4,47–49]
Correlation 2 from GLCM 1 p < 10 −4 69 54 12 37 [4,47–49]
Normalized Radial Length 5 (Entropy) < .0 0 04 34 14 21 21 [18,57]
Fourier Factor of Farthest Point < .0 0 04 21 21 66 39 [57,64]
Homogeneity from GLCM 1 < .002 36 20 48 68 [47,49]
F using Schabel Model and Weinmann AIF < .003 19 53 30 72 [36,43,44,50]
Table 3
Top-ten features with largest correlations with the class labels, their correlation values, and their rank in the proposed
feature fusion approach.
Feature name Correlation Rank in 1st Rank in 2nd Rank in 3rd Rank in 4th
coefficient fused fused fused fused
feature feature feature feature
Correlation 2 from GLCM 2 0.65 68 48 9 70
Cluster Prominence from GLCM 1 0.59 67 11 16 53
Centrifugal Features 0.57 9 3 4 10
Fourier Factor of Radial length 0.53 18 50 50 18
Correlation 1 from GLCM 1 0.38 70 9 13 2
Correlation 2 from GLCM 1 0.38 69 54 12 37
Normalized Radial Length 5 (Entropy) 0.33 34 14 21 21
Fourier Factor of Farthest Point 0.32 21 21 66 39
F using Schabel Model and Weinmann AIF 0.24 19 53 30 72
tc using Schabel Model and Parker AIF 0.23 15 67 25 63
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m
the same as the top-ten features selected by the t -test. Table 3
shows the list of the top-ten features selected by the correlation
analysis along with their correlation values and their ranks in the
proposed feature fusion approach.
It should be noted that the results of the above individual fea-
ture evaluation methods do not mean that other features do not
include any useful information for the classification of the sam-
ples. These results mean that the features used individually include
more relevant information than other features. Consequently, when
multi-dimensional feature spaces are explored, while the top-ten
features are still used as one of the top-ranking features, their con-
c
ributions (ranks) would be different from their individual ranks as
llustrated in Tables 2 and 3 .
.2. Validation of dataset and features
To validate our dataset and the extracted features, we have
dentified three previous studies [11,15,69] that have used a sub-
et of our proposed features. Due to restrictions in accessing the
atasets used by these studies, we have applied their classification
ethods to our dataset. The results obtained by our method are
ompared with the results of the original studies in Table 4 .
M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 161
Table 4
Application of three approaches (features and classification methods) used in similar studies to our dataset.
Study Number of cases used in the original study Number of features Reported accuracy Accuracy using our dataset (%)
Tsui et al. [11] 60 5 80.00% (Best result) 78.12
Huang et al. [15] 82 (44 benign, 51 malignant) 22 88.42% 90.63
Honda et al. [69] 90 (28 benign, 62 malignant) 11 85.6% 87.5
.
Input layer (4 Features ) . Output Layer (Diagnosis)
Fig. 10. The neural network structure used for classification.
Segmenta�on
Feature Extrac�on: Temporal,
Spa�al
Feature Fusion (KPCA)
Classifica�on: Benign,
Malignant
Fig. 11. Schema of the diagnosis process.
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The results listed in Table 4 indicate that the dataset and fea-
ures we used in this study are consistent with those used by the
revious studies and can obtain accuracy results that are similar to
hose reported by the other groups.
.3. Comparison of results of proposed methods using spatial
eatures, temporal features, and both
According to the details discussed in previous sections, two dif-
erent approaches are used to diagnose the disease and the results
re compared. The process starts with features extracted to repre-
ent spatial and temporal characteristics of the lesions. The initial
eature set includes 73 features. This feature set is then fed into
he feature fusion stage to obtain 4 features that carry the main
art of the information for diagnosis. This process is done on only
he training set in each process to avoid overfitting. The resulting
eature set is used for the classification process. The classifier is
neural network with three neurons in the hidden layer. Fig. 10
hows a schematic of the structure of the neural network.
The schema of the complete process is shown in Fig. 11 . The
rocess is run on a personal computer with an Intel ® Core TM i7
rocessor and 8 GB of RAM. The simplicity of the classification pro-
ess in the proposed method decreased the time of a single chain
f the process, including the feature extraction process, to less than
0 min.
To compare the proposed method with the methods discussed
n the previous studies, we developed a parallel method that in-
ludes a conventional feature reduction method along with a SVM
lassifier. We selected this classifier because it is a common clas-
ifier used in similar studies. In addition, to evaluate the vari-
nce or the confidence interval of the estimated performance mea-
ures (accuracy, true positive, true negative, false positive, and false
egative rates), we randomly selected 50 subsets of the original
ataset including 15 benign and 15 malignant lesions and applied
he classification methods. The means and standard deviations of
he resulting performance measures are listed in Table 5 . This ta-
le illustrates the superiority of the proposed method relative to
he reference method. It is notable that the false negative rate of
he proposed method is zero for all of the 50 data subsets, mean-
ng that our proposed method did not misclassify any of the ma-
ignant cases.
Next, to evaluate the roles of the spatial and temporal features
n classification, we applied both of the proposed method and the
eference method to these features separately.
We used the spatial features first. The results are presented in
able 6 . This table shows the spatial features, which generate di-
gnosis rates lower than those generated by the proposed compre-
ensive set of features.
Next, we used the temporal features. The results are presented
n Table 7 , showing the inferiority of the results to the above clas-
ifications that use the spatial features or both of the spatial and
emporal features.
. Discussion
Because of the risks and difficulties of the invasive diagnosis
ethods, diagnosis of the breast cancer using non-invasive meth-
ds has been a hot topic in recent years. However, none of the pro-
osed methods could guarantee a completely accurate diagnosis.
s discussed in the introduction section, physicians and engineers
ave worked on different imaging techniques along with different
rocessing approaches to reach accurate diagnosis systems. Mam-
ography and ultrasound images are mostly used only for prelim-
nary analysis. However, the quality of images is low and do not
nclude enough information about the lesions for the accurate di-
gnose and detection of their cancerous nature. DCE-MRI can pro-
ide a set of high-quality images from the lesions that include not
nly useful information about the shape of the suspected lesions
ut also useful information about the dynamic behavior of the con-
rast agent in the lesion. This dynamic information along with the
patial information about the lesion can make accurate diagnosis
ossible.
Most of the previous works in this field focus on one of the spa-
ial or temporal feature spaces. They sometimes include features
rom both spaces but focus on one of them as the key features.
ecause the nature of these two spaces is different, they include
omplementary information to improve the diagnosis process.
In 2008, Ertas et al. [17] studied segmentation and lesion de-
ection techniques for MR images using cellular neural networks
162 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164
Table 5
Comparison of the results of the proposed method and the reference method that uses a conventional feature reduction method along with
a SVM classifier.
Accuracy False positive rate True positive rate False negative rate True negative rate
Proposed fusion based method 98.87 ± 0.25% 2.13 ± 0.89% 97.88 ± 0.89% 0.00 ± 0.00% 10 0.0 0 ± 0.00%
Standard SVM based method 93.07 ± 0.49% 3.79 ± 2.10% 96.21 ± 2.10% 9.22 ± 1.20% 90.78 ± 1.20%
Table 6
Comparison of the results obtained using the spatial features only.
Accuracy False positive rate True positive rate False negative rate True negative rate
Proposed fusion based method 89.25 ± 0.16% 8.06 ± 0.35% 91.94 ± 0.35% 11.63 ± 0.20% 88.37 ± 0.20%
Standard SVM based method 91.00 ± 0.64% 13.24 ± 0.52% 86.76 ± 0.52% 3.53% ± 1.4% 96.47 ± 1.4%
Table 7
Comparison of the results obtained using the temporal features only.
Accuracy False positive rate True positive rate False negative rate True negative rate
Proposed fusion based method 72.93 ± 1.20% 30.81 ± 0.67% 69.19 ± 0.67% 20.46 ± 3.30% 79.54 ± 3.30%
Standard SVM based method 66.80 ± 0.59% 33.87 ± 0.53% 66.13 ± 0.53% 32.40 ± 0.82% 67.60 ± 0.82%
t
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and 3D template matching. They studied a dataset of images from
19 women and gained 97% correct segmentation. In 2012, Pang
et al. [18] used a two-step method, including fuzzy c-means and
snake algorithm, to segment MR images. They studied 60 mass-
like lesions and reached 97% correct segmentation. These stud-
ies confirm that recent segmentation techniques are not ideal and
the correct segmentation of the suspected lesions is still a chal-
lenge. Consequently, the classification methods that use the re-
sults of these methods might be inaccurate. Therefore, this study
avoided using fully automatic segmentation methods. In this study,
a semi-automatic method was used for the segmentation of the le-
sions. This method worked based on selecting an initial point by a
trained radiologist.
Some recent studies focused on dynamic features of enhance-
ment to determine the nature of the lesions. Ko et al. [19] exam-
ined some basic features for this purpose. They found a feature
called signal enhancement ratio that included meaningful infor-
mation about the lesion. Other studies focused on different types
of features. Yi et al. [51] focused on the correlation between the
model-based perfusion parameters and model-free parameters of
time-signal intensity curve of dynamic contrast enhanced MRI in
breast cancer patients. They found significant correlations among
the model-based and model-free parameters. This shows that fea-
tures extracted from same (dynamic) space would include almost
same information. Therefore, this study has focused on different
model based dynamic features to generate a relatively complete set
of dynamic features.
In this study, we used the most suitable features to obtain the
best classification of the benign and malignant lesions using dy-
namic MRI images of the breast. They included a comprehensive
set of spatial and temporal features. A statistical t -test and a corre-
lation analysis were used to evaluate the capability of the individ-
ual features in discriminating the benign and malignant cases. This
was done for obtaining insight into the usefulness of the features.
Then, all extracted features were combined using the proposed fu-
sion method.
The result of combination (fusion) of the proposed feature set
helped the classification methods achieve high classification rates.
The results of the proposed method showed a small false positive
rate with no false negatives. This is an indication that the proposed
features included sufficient information about the lesions. More-
over, since the results included false positives only, the proposed
method can serve as a diagnosis aid for the physicians. In addi-
tion, the proposed feature fusion method generated the features
shat simplified the classification process. The proposed classifica-
ion method used a three-layer feed-forward neural network with
hree neurons in the hidden layer.
After verifying the validity and ability of the proposed method,
he spatial and temporal features were evaluated separately. This
valuation showed that the results of none of these feature spaces
as comparable to the feature space generated by the integration
f the two spaces. It also showed that the spatial features outper-
ormed the temporal features.
Image quality naturally affects the image analysis results. To
his end, the accuracy of the proposed methods depends on the
uality of the information extracted from the DCE-MRI data. Noise
ecreases the quality of this information and thus deteriorates the
obustness of the methods. However, the proposed method is not
upposed to be highly sensitive to noise since it uses multiple spa-
iotemporal features and noise does not equally affect all features.
or example, the centrifugal/centripetal feature is minimally af-
ected by noise. Consequently, the accuracy of the proposed system
s expected to remain higher than the methods that use a small
umber of features in a single domain.
In future studies, we will compare the effect on noise on dif-
erent methods. We will also extend the proposed approach by ex-
ending the dataset and the feature set to achieve a robust and
ully automatic diagnosis method.
. Conclusion
Using information from different aspects of a disease can help
hysicians make an accurate diagnosis. This idea holds true for
omputerized diagnosis systems too. Because of the complexity of
alculating different types of features, most of the previous stud-
es focused on one aspect and tried to reach a correct diagnosis
hrough different classification methods. This study showed that
sing a comprehensive set of features extracted from the spatial
nd temporal information of the dynamic MRI images of the breast
esions and fusing the feature spaces in a smart manner avoided
omplex classification methods while generating an accurate diag-
osis. Moreover, the studies conducted on the proposed features
sing the methods used in the literature showed that selecting the
ight set of features played a crucial role in the process.
According to the results of the study, the proposed method may
erve as a solution for achieving an accurate computer-aided di-
gnosis system. However, it should be applied on a large dataset
o evaluate the robustness of the proposed methods for an actual
ystem.
M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 163
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onflict of interest
The authors declare no conflict of interest.
eferences
[1] A. Zeleniuch-Jacquotte , R.E. Shore , Epidemiology of Breast Cancer, in: D.F. Roses
(Ed.), Breast Cancer, Second Edition, Churchill Livingstone, Philadelphia, 2005,pp. 3–14 .
[2] M. Kallergi , K. Woods , L.P. Clarke , W. Qian , R.A. Clark , Image segmentation indigital mammography: comparison of local thresholding and region growing
algorithms, Comput Med Imaging Graph 16 (5) (1992) 323–331 .
[3] R. Sivaramakrishna , N.A. Obuchowski , W.A. Chilcote , K.A. Powell , Automaticsegmentation of mammographic density, Acad Radiol 8 (3) (2001) 250–256 .
[4] R.M. Rangayyan , S. Prajna , F.J. Ayres , J.E.L. Desautels , Detection of architecturaldistortion in prior screening mammograms using Gabor filters, phase portraits,
fractal dimension, and texture analysis, Int. J. Comput. Assist. Radiol. and Surg.2 (6) (2008) 347–361 .
[5] P. Kus , I. Karagoz , Fully automated gradient based breast boundary detection
for digitized X-ray mammograms, Comput. Biol. Med. 42 (1) (2012) 75–82 . [6] J. Kilday , F. Palmieri , M.D. Fox , Classifying mammographic lesions using com-
puterized image analysis, IEEE Trans. Med. Imaging 12 (4) (1993) 664–669 . [7] R.-F. Chang , W.-J. Wu , W.K. Moon , W.-M. Chen , W. Lee , D.-R. Chen , Segmenta-
tion of breast tumor in three-dimensional ultrasound images using three-di-mensional discrete active contour model, Ultrasound Med. Biol. 29 (11) (2003)
1571–1581 .
[8] H. Fujita , Y. Uchiyama , T. Nakagawa , D. Fukuoka , Y. Hatanaka , T. Hara , G.N. Lee ,Y. Hayashi , Y. Ikedo , X. Gao , X. Zhou , Computer-aided diagnosis: the emerging
of three CAD systems induced by Japanese health care needs, Comput. Meth-ods Programs Biomed. 92 (3) (2008) 238–248 .
[9] H.D. Cheng , J. Shan , W. Ju , Y. Guo , L. Zhang , Automated breast cancer detectionand classification using ultrasound images: a survey, Pattern Recognit. 43 (1)
(2010) 299–317 . [10] A. Jalalian , S.B. Mashohor , H.R. Mahmud , M.I. Saripan , A.R. Ramli , B. Karasfi,
Computer-aided detection/diagnosis of breast cancer in mammography and ul-
trasound: a review, Clin. Imaging 37 (3) (2013) 420–426 . [11] P.H. Tsui , Y.Y. Liao , C.C. Chang , W.H. Kuo , K.J. Chang , C.K. Yeh , Classification
of benign and malignant breast tumors by 2-d analysis based on contour de-scription and scatterer characterization, IEEE Trans. Med. Imag. 29 (2) (2010)
513–522 . [12] H.R. Kim , H.K. Jung , K.H. Ko , S.J. Kim , K.S. Lee , Mammography, US, and MRI for
preoperative prediction of extensive intraductal component of invasive breast
cancer: interobserver variability and performances, Clinical Breast Cancer 16(4) (2016) 305–311 .
[13] S.J. Hyun , E.K. Kim , J.H. Yoon , H.J. Moon , M.J. Kim , Adding MRI to ultrasoundand ultrasound-guided fine-needle aspiration reduces the false-negative rate
of axillary lymph node metastasis diagnosis in breast cancer patients, ClinicalRadiol. 70 (7) (2015) 716–722 .
[14] F. Zaiton , S.M. Shehata , M.H. Abo Warda , M.A. Alekrashy , Diagnostic value of
MRI for predicting axillary lymph nodes metastasis in newly diagnosed breastcancer patients: diffusion-weighted MRI, Egypt. J. Radiol. Nuclear Med. (2016) .
[15] Y.-H. Huang , Y.-C. Chang , C.-S. Huang , T.-J. Wu , J.-H. Chen , R.-F. Chang , Com-puter-aided diagnosis of mass-like lesion in breast MRI: Differential analysis
of the 3-D morphology between benign and malignant tumors, Comput. Meth.Programs Biomed. 112 (3) (2013) 508–517 .
[16] T.E. Yankeelov , M. Lepage , A. Chakravarthy , E.E. Broome , K.J. Niermann ,
M.C. Kelley , I. Meszoely , I.A. Mayer , C.R. Herman , K. McManus , R.R. Price ,J.C. Gore , Integration of quantitative DCE-MRI and ADC mapping to monitor
treatment response in human breast cancer: initial results, Magn. Reson Imag-ing 25 (1) (2007) 1–13 .
[17] G. Ertas , H.O. Gulcur , O. Osman , O.N. Ucan , M. Tunaci , M. Dursun , Breast MRsegmentation and lesion detection with cellular neural networks and 3D tem-
plate matching, Comput. Biol. Med. 38 (1) (2008) 116–126 .
[18] Y. Pang , L. Li , W. Hu , Y. Peng , L. Liu , Y. Shao , Computerized segmentation andcharacterization of breast lesions in dynamic contrast-enhanced MR images
using fuzzy c-means clustering and snake algorithm, Comput. Math. MethodsMed 2012 (2012) 634907 .
[19] M.-S. Ko , S.H. Kim , B.J. Kang , B.G. Choi , B.J. Song , E.S. Cha , A.P. Kiraly , I.S. Kim ,A method to quantify breast MRI for predicting tumor invasion in patients
with preoperative biopsy- proven ductal carcinoma in situ (DCIS), J. Korean
Soc. Magn. Reson. Med. 17 (2) (2013) 73 . 20] S.A. Moestue , E.M. Huuse , E.M. Lindholm , A. Bofin , O. Engebraaten , G.M. Mae-
landsmo , L.A. Akslen , I.S. Gribbestad , Low-molecular contrast agent dynamiccontrast-enhanced (DCE)-MRI and diffusion-weighted (DW)-MRI in early as-
sessment of bevacizumab treatment in breast cancer xenografts, J. Magn. Re-son. Imaging 38 (5) (2013) 1043–1053 .
[21] R.G. Abramson , X. Li , T.L. Hoyt , P.F. Su , L.R. Arlinghaus , K.J. Wilson , V.G. Abram-son , A.B. Chakravarthy , T.E. Yankeelov , Early assessment of breast cancer re-
sponse to neoadjuvant chemotherapy by semi-quantitative analysis of high-
-temporal resolution DCE-MRI: preliminary results, Magn. Reson. Imaging 31(9) (2013) 1457–1464 .
22] N.C. Atuegwu , L.R. Arlinghaus , X. Li , A.B. Chakravarthy , V.G. Abramson ,M.E. Sanders , T.E. Yankeelov , Parameterizing the logistic model of tumor
growth by DW-MRI and DCE-MRI data to predict treatment response and
changes in breast cancer cellularity during neoadjuvant chemotherapy, Transl.Oncol. 6 (3) (2013) 256–264 .
23] S. Bahri , J.H. Chen , H.J. Yu , A. Kuzucan , O. Nalcioglu , M.Y. Su , Can dynamic con-trast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node sta-
tus in patients with breast cancer? Ann. Oncol. 19 (4) (2008) 822–824 . [24] O.I. Craciunescu , K.L. Blackwell , E.L. Jones , J.R. Macfall , D. Yu , Z. Vujaskovic ,
T.Z. Wong , V. Liotcheva , E.L. Rosen , L.R. Prosnitz , T.V. Samulski , M.W. Dewhirst ,DCE-MRI parameters have potential to predict response of locally advanced
breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pi-
lot study, Int. J. Hyperthermia 25 (6) (2009) 405–415 . 25] S. Drisis , T. Metens , M. Ignatiadis , K. Stathopoulos , S.L. Chao , M. Lemort , Quan-
titative DCE-MRI for prediction of pathological complete response follow-ing neoadjuvant treatment for locally advanced breast cancer: the impact of
breast cancer subtypes on the diagnostic accuracy, Eur. Radiol. 26 (5) (2016)1474–1484 .
26] V. Garimella , O. Qutob , J.N. Fox , E.D. Long , A. Chaturvedi , L.W. Turnbull ,
P.J. Drew , Recurrence rates after DCE-MRI image guided planning for breast--conserving surgery following neoadjuvant chemotherapy for locally advanced
breast cancer patients, Eur. J. Surg. Oncol. 33 (2) (2007) 157–161 . [27] L.R. Jensen , E.M. Huuse , T.F. Bathen , P.E. Goa , A.M. Bofin , T.B. Pedersen , S. Lund-
gren , I.S. Gribbestad , Assessment of early docetaxel response in an experimen-tal model of human breast cancer using DCE-MRI, ex vivo HR MAS, and in vivo
1H MRS, NMR Biomed. 23 (1) (2010) 56–65 .
28] R. Johansen , L.R. Jensen , J. Rydland , P.E. Goa , K.A. Kvistad , T.F. Bathen , D.E. Axel-son , S. Lundgren , I.S. Gribbestad , Predicting survival and early clinical response
to primary chemotherapy for patients with locally advanced breast cancer us-ing DCE-MRI, J. Magn. Reson. Imaging 29 (6) (2009) 1300–1307 .
29] X. Li , L.R. Arlinghaus , G.D. Ayers , A.B. Chakravarthy , R.G. Abramson ,V.G. Abramson , N. Atuegwu , J. Farley , I.A. Mayer , M.C. Kelley , I.M. Meszoely ,
J. Means-Powell , A.M. Grau , M. Sanders , S.R. Bhave , T.E. Yankeelov , DCE-MRI
analysis methods for predicting the response of breast cancer to neoadju-vant chemotherapy: pilot study findings, Magn. Reson. Med. 71 (4) (2014)
1592–1602 . 30] L.B. Nilsen , A. Fangberget , O.M. Geier , O. Engebraaten , E. Borgen , D.R. Olsen ,
T. Seierstad , Associations between tumor vascularization assessed by in vivoDCE-MRI and the presence of disseminated tumor cells in bone marrow in
breast cancer patients at the time of diagnosis, J. Magn. Reson. Imaging 40 (6)
(2014) 1382–1391 . [31] M.D. Pickles , M. Lowry , D.J. Manton , L.W. Turnbull , Prognostic value of
DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy:a comparison with traditional survival indicators, Eur. Radiol. 25 (4) (2015)
1097–1106 . 32] M.D. Pickles , D.J. Manton , M. Lowry , L.W. Turnbull , Prognostic value of
pre-treatment DCE-MRI parameters in predicting disease free and overall sur-
vival for breast cancer patients undergoing neoadjuvant chemotherapy, Eur. J.Radiol. 71 (3) (2009) 498–505 .
[33] S. Siamakpour-Reihani , K. Owzar , C. Jiang , P.M. Scarbrough , O.I. Craciunescu ,J.K. Horton , H.K. Dressman , K.L. Blackwell , M.W. Dewhirst , Genomic profiling
in locally advanced and inflammatory breast cancer and its link to DCE-MRIand overall survival, Int. J. Hyperthermia 31 (4) (2015) 386–395 .
34] A. Tudorica , K.Y. Oh , S.Y. Chui , N. Roy , M.L. Troxell , A . Naik , K.A . Kemmer ,Y. Chen , M.L. Holtorf , A. Afzal , C.S. Springer Jr. , X. Li , W. Huang , Early Predic-
tion and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy
Using Quantitative DCE-MRI, Transl. Oncol. 9 (1) (2016) 8–17 . [35] N. Tuncbilek , F. Tokatli , S. Altaner , A. Sezer , M. Ture , I.K. Omurlu , O. Temizoz ,
Prognostic value DCE-MRI parameters in predicting factor disease free survivaland overall survival for breast cancer patients, Eur. J. Radiol. 81 (5) (2012)
863–867 . 36] C.H. Wang , F.F. Yin , J. Horton , Z. Chang , Review of treatment assessment using
DCE-MRI in breast cancer radiation therapy, World J. Methodol. 4 (2) (2014)
46–58 . [37] M.L. Giger , Computerized analysis of images in the detection and diagnosis of
breast cancer, Semin. Ultrasound CT MR 25 (5) (2004) 411–418 . 38] S. Behrens , H. Laue , M. Althaus , T. Boehler , B. Kuemmerlen , H.K. Hahn ,
H.O. Peitgen , Computer assistance for MR based diagnosis of breast cancer:present and future challenges, Comput. Med. Imag. Graph 31 (4-5) (2007)
236–247 .
39] J. Pan , B.E. Dogan , S. Carkaci , L. Santiago , E. Arribas , S.B. Cantor , W. Wei ,R.J. Stafford , G.J. Whitman , Comparing performance of the CADstream and the
DynaCAD breast MRI CAD systems: CADstream vs. DynaCAD in breast MRI, J.Digit Imag. 26 (5) (2013) 971–976 .
40] F. Montemurro , L. Martincich , I. Sarotto , I. Bertotto , R. Ponzone , L. Cellini ,S. Redana , P. Sismondi , M. Aglietta , D. Regge , Relationship between DCE-MRI
morphological and functional features and histopathological characteristics of
breast cancer, Eur. Radiol. 17 (6) (2007) 1490–1497 . [41] E.A. Morris, C.H. Lee, et al., ACR BI-RADS ® magnetic resonance imaging. In:
ACR BI-RADS ® Atlas, Breast Imaging Reporting and Data System. 2013. 42] E. Henderson , B.K. Rutt , T.Y. Lee , Temporal sampling requirements for the
tracer kinetics modeling of breast disease, Magn. Reson. Imaging 16 (9) (1998)1057–1073 .
43] P. Armitage , C. Behrenbruch , M. Brady , N. Moore , Extracting and visualiz-
ing physiological parameters using dynamic contrast-enhanced magnetic reso-nance imaging of the breast, Med. Image Anal. 9 (4) (2005) 315–329 .
44] G.J.S. Litjens , M. Heisen , J. Buurman , B.M. ter Haar Romeny , Pharmacokineticmodels in clinical practice: what model to use for DCE-MRI of the breast? in:
164 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164
Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging:From Nano to Macro, 2010 .
[45] R.H. El Khouli , K.J. Macura , I.R. Kamel , M.A. Jacobs , D.A. Bluemke , 3-T dy-namic contrast-enhanced MRI of the breast: pharmacokinetic parameters ver-
sus conventional kinetic curve analysis, AJR Am. J. Roentgenol. 197 (6) (2011)1498–1505 .
[46] G. Agrawal , M.Y. Su , O. Nalcioglu , S.A. Feig , J.H. Chen , Significance of breastlesion descriptors in the ACR BI-RADS MRI lexicon, Cancer 115 (7) (2009)
1363–1380 .
[47] R.M. Haralick , K. Shanmugam , I.H. Dinstein , Textural features for image classi-fication, IEEE Trans. Syst. Man Cybern. 3 (6) (1973) 610–621 .
[48] L.K. Soh , C. Tsatsoulis , Texture analysis of SAR sea ice imagery using graylevel co-occurrence matrices, IEEE Trans. Geosci. Remote Sens. 37 (2) (1999)
780–795 . [49] D.A. Clausi , Y. Zhao , Rapid extraction of image texture by co-occurrence using
a hybrid data structure, Comput. Geosci. 28 (6) (2002) 763–774 .
[50] E. Eyal , H. Degani , Model-based and model-free parametric analysis of breastdynamic-contrast-enhanced MRI, NMR Biomed. 22 (1) (2009) 40–53 .
[51] B. Yi , D.K. Kang , D. Yoon , Y.S. Jung , K.S. Kim , H. Yim , T.H. Kim , Is there anycorrelation between model-based perfusion parameters and model-free pa-
rameters of time-signal intensity curve on dynamic contrast enhanced MRI inbreast cancer patients? Eur. Radiol. 24 (5) (2014) 1089–1096 .
[52] P.S. Tofts , Modeling tracer kinetics in dynamic Gd-DTPA MR imaging, J. Magn.
Reson. Imaging 7 (1) (1997) 91–101 . [53] P.S. Tofts , G. Brix , D.L. Buckley , J.L. Evelhoch , E. Henderson , M.V. Knopp ,
H.B.W. Larsson , T.-Y. Lee , N.A. Mayr , G.J.M. Parker , R.E. Port , J. Taylor ,R.M. Weisskoff, Estimating kinetic parameters from dynamic contrast-en-
hanced t1-weighted MRI of a diffusable tracer: standardized quantities andsymbols, J. Magn. Reson. Imag. 10 (3) (1999) 223–232 .
[54] G. Brix , F. Kiessling , R. Lucht , S. Darai , K. Wasser , S. Delorme , J. Griebel , Micro-
circulation and microvasculature in breast tumors: pharmacokinetic analysis ofdynamic MR image series, Magn. Reson. Med. 52 (2) (2004) 420–429 .
[55] K.S.S. Lawrence , T.-Y. Lee , An adiabatic approximation to the tissue homogene-ity model for water exchange in the brain: I. theoretical derivation, J. Cerebral
Blood Flow Metabol. 18 (12) (1998) 1365–1377 . [56] K.S.S. Lawrence , T.-Y. Lee , An adiabatic approximation to the tissue homogene-
ity model for water exchange in the brain: ii. experimental validation, J. Cere-
bral Blood Flow Metabol. 18 (12) (1998) 1378–1385 .
[57] L. Bahreini , E. Fatemizadeh , M. Gity , Diagnostic efficacy of all series of dynamiccontrast enhanced breast MR images using gradient vector flow (GVF) segmen-
tation and novel border feature extraction for differentiation between malig-nant and benign breast lesions, Iran J. Radiol. 7 (04) (2010) 225–234 .
[58] F. Castanedo , A review of data fusion techniques, Sci. World J. 2013 (2013)704504 .
[59] P. Perakis , T. Theoharis , I.A. Kakadiaris , Feature fusion for facial landmark de-tection, Pattern Recognit. 47 (9) (2014) 2783–2793 .
[60] D. Wang , X. Wang , S. Kong , Integration of multi-feature fusion and dictionary
learning for face recognition, Image Vis. Comput. 31 (12) (2013) 895–904 . [61] G. Lin , H. Zhu , X. Kang , C. Fan , E. Zhang , Multi-feature structure fusion of
contours for unsupervised shape classification, Pattern Recognit. Lett. 34 (11)(2013) 1286–1290 .
[62] X. Wang , Q. Wu , X. Lin , Z. Zhuo , L. Huang , Pedestrian identification basedon fusion of multiple features and multiple classifiers, Neurocomputing 188
(2016) 151–159 .
[63] B. Schölkopf , A. Smola , K.-R. Müller , et al. , Kernel principal component anal-ysis, in: W. Gerstner, et al. (Eds.), Proceedings of the 7th International Con-
ference on Artificial Neural Networks — ICANN’97: Lausanne, Switzerland,October 8–10, Berlin, Heidelberg, Editors, Springer Berlin Heidelberg, 1997,
pp. 583–588 . [64] Y.-H. Liu , Y.-T. Hsiao , W.-T. Cheng , Y.-C. Liu , J.-Y. Su , Low-resolution tactile im-
age recognition for automated robotic assembly using kernel PCA-based fea-
ture fusion and multiple kernel learning-based support vector machine, Math.Probl. Eng. 2014 (2014) 11 .
[65] D. Sarwinda , A.M. Arymurthy , Feature selection using kernel PCA forAlzheimer’s disease detection with 3D MR images of brain, in: Proceedings of
the 2013 International Conference Advanced Computer Science and Informa-tion Systems (ICACSIS), 2013 .
[66] N. Yu , P. Jiao , Handwritten digits recognition approach research based on dis-
tance & Kernel PCA, in: Proceedings of the 2012 IEEE Fifth International Con-ference on Advanced Computational Intelligence (ICACI), 2012 .
[67] G.W.A. SNEDECOR , W.G.A. COCHRAN , Statistical Methods, Iowa State UniversityPress, 1967 .
[68] M. Natrella , et al. , NIST/SEMATECH E-Handbook of Statistical Methods, in:C. Croarkin, et al. (Eds.), NIST/SEMATECH, 2010 .
[69] E. Honda , R. Nakayama , H. Koyama , A. Yamashita , Computer-aided diagnosis
scheme for distinguishing between benign and malignant masses in breastDCE-MRI, J. Digital Imag. 29 (3) (2016) 388–393 .