Mammograms Feature Extraction using Fuzzy Surface · Mammograms Feature Extraction using Fuzzy Surface. Author(s): Dubey R B, Nagpal A . Abstract. Mammography is the most contemporary

Article ID: WMC002327 2046-1690

Mammograms Feature Extraction using FuzzySurfaceCorresponding Author:Dr. Rash B Dubey,Professor, ECE Dept, Hindu College of Engg, Sonepat, 121003 - India

Submitting Author:Dr. Rash B Dubey,Professor, ECE Dept, Hindu College of Engg, Sonepat, 121003 - India

Article ID: WMC002327

Article Type: Research articles

Submitted on:16-Oct-2011, 03:05:14 PM GMT Published on: 17-Oct-2011, 10:42:34 AM GMT

Article URL: http://www.webmedcentral.com/article_view/2327

Subject Categories:BREAST

Keywords:Breast cancer, fuzzy surface, feature selection and features extraction.

How to cite the article:Dubey R B, Nagpal A . Mammograms Feature Extraction using Fuzzy Surface .WebmedCentral BREAST 2011;2(10):WMC002327

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Mammograms Feature Extraction using FuzzySurfaceAuthor(s): Dubey R B, Nagpal A

Abstract

Mammography is the most contemporary option forthe premature detection of breast cancer in women.The principal feature within the breast region is thebreast contour. Extraction of the breast region anddelineation of the breast contour is an essentialpre-processing step in the process of computer aideddetection. Primarily it allows the search forabnormalities to be limited to the region of the breastwithout undue influence from the background of themammogram. The methodology involves the use offuzzy surface for selecting the features ofmammograms. Feature extraction is an essentialpre-processing step to pattern recognition andmachine learning problems. It is often decomposedinto feature construction and feature selection. It iswell known that mammographic images have somedegrees of fuzziness such as indistinct borders,ill-defined shapes, and different densities. Due to thenature of mammography and breast structure, fuzzylogic would be a better choice to handle the fuzzinessof mammograms than traditional methods. There aremany features such as shape features, texturefeatures etc. The surface viewer is used to display thedependency of one of the outputs on any one or two ofthe inputs — that is, it generates and plots an outputsurface map for the system. A variety of samples hasbeen tried out to generate and plot the surface maps.

Introduction

1. IntroductionBreast cancer is the most common type of cancerfound in women. It is the most frequent form of cancerand one in 22 women in India is likely to suffer frombreast cancer [1]. Breast cancer is the leading causeof death among women in many countries. Detecting abreast cancer at the earliest stage has the mostimportant impact on prognosis. Mammography is themost cost effective method to detect early signs ofbreast cancer [2] and is the most contemporary optionfor the premature detection of breast cancer in women.Breast cancer is considered as one of the primarycauses of women mortality. The mortality rate inasymptotic women can be brought down with the aid

of premature diagnosis. Despite the increasingnumber of cancers being diagnosed, the death ratehas been reduced remarkably in past decade due tothe screening programs. Premature detection of breastcancer increases the prospect of survival whereasdelayed diagnosis frequently confronts the patient toan unrecoverable stage and results in death [3]. So far,many systems have been developed to detect themicro-calcification (MCC) in mammograms. Theyusually detect suspicious regions first and thentechniques can be applied to the features of theseregions. The existing features for detecting the MCCcould be divided into several branches such as shapefeatures, statistical texture features, wavelet featuresand etc [4]. Two recent advances in mammographyinclude digital mammography and computer-aideddetection. Various techniques have been designed forthe detection of breast cancer, but all of them areusing either genetic algorithm or CAD technology. Theuse of fuzzy logic is to deal with uncertainty fordiagnosis risk status of breast cancer [5].The remainder of this paper is structured in foursections. In Sections II details of the proposedmethodology are presented. Implementation isdescribed in Section III. Results discussions are drawnin Section IV.

Methods

2. Proposed MethodologyThe proposed methodology is outlined in Fig. 1.2.1 Pre-processingThe image pre-processing refers to the initialprocessing of raw image to correct the geometricdistortions, calibrate the data radio metrically andeliminate the noise and clouds that present in the data.These operations are called pre-processing becausethey normally carried out before the real analysis andmanipulations of the data occur in order to extract anyspecific information. The aim is to correct the distortedor degraded image data to create a more faithfulrepresentation of the real scene.The purpose of pre-processing is to remove noise andradiopaque artifacts contained within the mammogramand increase region homogeneity, with the objectivebeing to improve in algorithm reliability and robustness.Mammograms often contain artifacts in the form of



identification labels, markers, and wedges in theunexposed air-background (non-breast) region. Suchartifacts are usually radiopaque in the sense that theyare not transparent to radiation. One of the problemswith precise segmentation of the breast region is thatthe existence of such artifacts often results in anon-uniform background region which may cause asegmentation algorithm to fail.Fig.1: Flowchart.2.2 Fuzzy logic Fuzzy logic provides a means of calculatingintermediate values between absolute true andabsolute false with resulting values ranging between0.0 and 1.0. It seeks to handle the concepts of partialtruth by creating values representing what is betweentotal truth and total false. Fuzzy logic differs fromBoolean logic in that it is permissive of naturallanguage queries and is more like human thinking; it isbased on degrees of truth [13-15]. 2.2.1 Fuzzy rule baseFuzzy rule-based approach to modelling is based onverbally formulated rules overlapped throughout theparameter space. They use numerical interpolation tohandle complex non-linear relationships. Fuzzy rulesare linguistic IF-THEN- constructions that have thegeneral form "IF A THEN B" where A and B arepropositions containing linguistic variables. A is calledthe premise and B is the consequence of the rule. Ineffect, the use of linguistic variables and fuzzyIF-THEN- rules exploits the tolerance for imprecisionand uncertainty. In this respect, fuzzy logic mimics thecrucial ability of the human mind to summarize dataand focus on decision-relevant information [13-15].2.2.2 Fuzzy surfaceSurface viewer is a read-only editor. The rule viewerand the surface viewer are strictly read-only tools. Therule viewer is used as a diagnostic; it can show whichrules are active, or how individual membershipfunction shapes are influencing the results. Thesurface viewer is used to display the dependency ofone of the outputs on any one or two of the inputs thatit generates and plots an output surface map for thesystem.Upon opening the surface viewer, a two-dimensionalcurve represents the mapping from service quality totip amount. Since this is a one-input one-output case,we can see the entire mapping in one plot. Two- inputone-output systems also work well, as they generatethree-dimensional plots that MATLAB can adeptlymanage. When we move beyond three dimensionsoverall, we start to encounter trouble displaying theresults. Accordingly, the surface viewer is equippedwith pop-up menus that select any two inputs and anyone output for plotting. Just below the pop-up menus

are two text input fields that determine how manyx-axis and y-axis gridlines one want to include. Thisallows keeping the calculation time reasonable forcomplex problems. To change the x-axis or y-axis gridafter the surface is in view, simply change theappropriate text field, and click on either X-grids orY-grids, according to which text field one want tochange, to redraw the plot [13-15].2.3 Feature selectionFeature selection is an important part of anyclassification scheme. The success of a classificationscheme largely depends on the features selected andthe extent of their role in the model. The objective ofperforming feature selection is three fold: (a) improvingthe prediction performance of the predictors, (b)providing faster and more cost effective predictors and(c) providing a better understanding of the processesthat generated the data. There are many benefits ofvariable and feature selection: it facilitates datavisualization and understanding, reduces the storagerequirements, reduces training times and improvesprediction performance.There are various features of mammograms such astexture, shape features etc., out of all these, there arefurther various categories of these features. Thefeatures which are selected for the fuzzy surfaceimplementation are contour, lines and irregularboundary etc.A mammogram contains two distinctive regions: theexposed breast region and the unexposedair-background (non-breast) region. The principalfeature on a mammogram is the breast contour,otherwise known as the skin-air interface, or breastboundary. The breast contour can be obtained bypartitioning the mammogram into breast andnon-breast regions. The extracted breast contourshould adequately model the soft-tissue/air interfaceand preserve the nipple in profile.The largest single feature on a mammogram is theskin-air interface, or breast contour. Extraction of thebreast contour is useful for a number of reasons.Foremost it allows the search for abnormalities to belimited to the region of the breast without undueinfluence from the background of the mammogram.Segmentation of the breast-region from thebackground is made difficult by the tapering nature ofthe breast, such that the breast contour lies inbetween the soft tissue and the non-breast region. Theprecise segmentation of the breast region inmammograms is an essential preprocessing step inthe computer-aided analysis of mammograms for anumber of reasons [4-5,7-9].2.4 Graphical User Interface (GUI)A GUI allows a computer user to move from



application to application. A good GUI makes anapplication easy, practical and efficient to use and themarketplace success of today's software programsdepends on good GUI design.In computing a GUI is a type of user interface thatallows users to interact with electronic devices withimages rather than text commands. GUIs can be usedin computers, hand-held devices such as MP3 players,portable media players or gaming devices, householdappliances and office equipment. A GUI representsthe information and actions available to a user throughgraphical icons and visual indicators such assecondary notation, as opposed to text-basedinterfaces, typed command labels or text navigation.The actions are usually performed through directmanipulation of the graphical elements. The term GUIis h is tor ica l ly rest r ic ted to the scope oftwo-dimensional display screens with displayresolutions able to describe generic information.GUIDE stores GUIs in two files, which are generatedfor the first time when GUI is saved or run, the twofiles are namely; fig file which contains a completedescription of the GUI figure Layout and thecomponents of the GUI and m file which contains thecode that controls the GUI.2.5 Edge detectionEdge detection is a fundamental tool in imageprocessing and computer vision, particularly in theareas of feature detection and feature extraction,which aim at identifying points in a digital image atwhich the image brightness changes sharply or, moreformally, has discontinuities.The purpose of detecting sharp changes in imagebrightness is to capture important events and changesin properties of the world. It can be shown that underrather general assumptions for an image formationmodel, discontinuities in image brightness are likely tocorrespond such as discontinuities in depth,discontinuities in surface orientation, changes inmaterial properties and variations in scene illumination.There are many methods for edge detection. Cannyedge detection is us here. John Canny considered themathematical problem of deriving an optimalsmoothing filter given the criteria of detection,localization and minimizing multiple responses to asingle edge. He showed that the optimal filter giventhese assumptions is a sum of four exponential terms.He also showed that this f i l ter can be wellapproximated by first-order derivatives of Gaussians.Canny also introduced the notion of non-maximumsuppression, which means that given thepre-smoothing filters, edge points are defined aspoints where the gradient magnitude assumes a localmaximum in the gradient direction [6].

2.5.1 Thresholding and linkingOnce we have computed a measure of edge strength(typically the gradient magnitude), the next stage is toapply a threshold, to decide whether edges arepresent or not at an image point. The lower thethreshold, the more edges will be detected, and theresult will be increasingly susceptible to noise anddetecting edges of irrelevant features in the image.Conversely a high threshold may miss subtle edges,or result in fragmented edges.If the edge thresholding is applied to just the gradientmagnitude image, the resulting edges will in generalbe th ick and some type of edge th inningpost-processing is necessary. For edges detected withnon-maximum suppression however, the edge curvesare thin by definition and the edge pixels can be linkedinto edge polygon by an edge linking (edge tracking)procedure. On a discrete grid, the non-maximumsuppression stage can be implemented by estimatingthe gradient direction using first-order derivatives, thenrounding off the gradient direction to multiples of 45degrees, and finally comparing the values of thegradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem ofappropriate thresholds for thresholding is by usingthresholding with hysteresis. This method usesmultiple thresholds to find edges. We begin by usingthe upper threshold to find the start of an edge. Oncewe have a start point, we then trace the path of theedge through the image pixel by pixel, marking anedge whenever we are above the lower threshold. Westop marking our edge only when the value falls belowour lower threshold. This approach makes theassumption that edges are likely to be in continuouscurves, and allows us to follow a faint section of anedge we have previously seen, without meaning thatevery noisy pixel in the image is marked down as anedge. Still, however, we have the problem of choosingappropriate thresholding parameters, and suitablethresholding values may vary over the image.2.5.2 Edge thinningEdge thinning is a technique used to remove theunwanted spurious points on the edge of an image.This technique is employed after the image has beenfiltered for noise (using median, Gaussian filter etc.),the edge operator has been applied to detect theedges and after the edges have been smoothed usingan appropriate threshold value. This removes all theunwanted points and if applied carefully, results in onepixel thick edge elements. Sharp and thin edges leadto greater efficiency in object recognition.

Imlementation



3. ImplementationThe algorithm works as follows:• First of all input image is taken from database andthen region of interest is selected. • Boundaries are made with the help of edge detectionalgorithms. The classification measures are taken asthe input of a fuzzy decision making process with twoinputs and one output, purpose is to calculate thedegree of membership to which the pixel belongs tothe four types (contour, region, etc).• Fuzzy logic is applied to the classification measures.The input space of the linguistic variable is comprisedof the three fuzzy sets {low, med, high}, and iscomprised of two fuzzy sets labelled {low, high}. Onthe basis of the low, medium and high logic, groupscan be distinguished.• Once an approximation of the breast region has beenderived from the fuzzy segmentation, regions can beclassified during the processing. Using fuzzy logicconcept, the features used for fuzzy rule are contour,lines and irregular boundary of an image, the range isspecified there are 3-input ranges one for finding thecontour and other one for lines and last one is fordefining irregular boundary. • The rule base is defined and fuzzy surface is viewedand the m-file is linked with fis file of MATLAB. In fisfile inputs are selected as; Input range 1: 30-50, Inputrange 2: 50-80 and Input range 3: 80-100. • In output, there will be three membership functiondefined one for contour, second one for irregularboundary and third one for lines, these are thefeatures are selected for fuzzy rule base and these willbe grouped on the basis of range and then classifiedusing segmentation as Output 1: Contour: Range30-50, Output 2: Irregular boundary: 50-80 and Output3: lines: 80-100.The various other steps involved in making fuzzy rulefile are describes as under: -The first step to make fis file is to define the input andoutput of particular design. After defining input andoutput ranges, then fuzzy rules are defined based onfeature selection. The features selected are contour,irregular boundary and lines. Fis editor file and fuzzyrule base are shown in Fig 2 and Fig. 3.Fig 2: Fis Editor file in MATLAB Fig. 3: Fuzzy rule base - As the rules are defined within the range specefied,after this rules can be viewed in MATLAB. The outputcan be checked for different combinations and thenfeature selection can be done. The rules are viewed,can be checked for different configuration by changingthe input values, the output can be checked. Once

the rules have been defined the surface can beviewed.The Fuzzy rule viewer and Fuzzy surfaceviewer are shown in Fig. 4 and Fig. 5.Fig. 4: Fuzzy rule viewer Fig 5: Fuzzy surface viewer- As the surface is viewed, the contour obtained isshown in Fig. 6. After linking the fis file with m-file, theGUI will appear as shown in Fig. 7, in the screen ofGUI image can be browse and then it can be viewusing the fuzzy technique. The features contour,irregular boundary and lines are calculated. The outputwith contour and lines are found maximum at 229.This is shown in Fig. 8. Fig 6: Fuzzy surface contour Fig 7: Screen of GUI Implementation in MATLAB Fig 8: GUI Implementation of image (mdb001.jpg)

References

1.B. W Hong, S. Soatto and M. Mellor, “Combiningtopological and geometric features of mammograms todetect masses”, University of California Los Angeles,L.A., U.S.A, 2008.2.R. N. Panda, B. K Panigrahi and M. R. Patro,“Feature ext ract ion for c lass i f icat ion ofmicroca lc i f icat ions and mass les ions inmammograms”, IJCSNS International Journal ofComputer Science and Network Security, vol. 9, no. 5,pp. 255-265, 2009.3.Z. Q. Wu, J. Jiang and Y. H. Peng, “Effectivefeatures based on normal linear structures fordetecting microcalcifications in mammograms”, IEEEIntl. Conf., 2008.4.M. Vasantha, V. S. Bharathi and R. Dhamodharan,“Medical image feature extraction, selection andclassification”, International Journal of EngineeringScience and Technology, vol. 2, no. 6, pp. 2071-2076,2010.5.A. A. E. Saleh, S. E. Barakat and A. A. E. Awad, “Afuzzy decision support system for management ofbreast cancer”, International Journal of AdvancedComputer Science and Applications, vol. 2, no.3, pp.34-40, 2011.6.F. Sahba, and A. Venetsanopoulos, “A Novel basedframework for detection of clustered microcalcificationin mammograms”, IEEE Intl. Conf., pp. 1-6, 2010.7.J.C Bezdek,and R.Chandrasekhar, “A geometricapproach to edge detection”, IEEE Transactions onFuzzy Systems, vol. 6, no. 1, pp. 52-75, 1998.8. A. Dong and B. Wang, “Feature selection andanalysis on mammogram classification”, IEEE IntlConf., pp. 731-735, 2009.9.D. Wang, J. Ren, J. Jiang & S.S. Ipson, “Applying



feature selection for effective classification ofmicrocalcification clusters in mammograms”, IEEE Intl.Conf., pp. 1384-1387, 2010.10.M. A. Alolfe , W. A. Mohamed , Y. M. Kadah and A.S. Mohamed, “Feature selection in computer aideddiagnostic system for microcalcification detection indigital mammograms”, 26th NRSC 2009, FutureUniversity, 5th Compound, New Cairo, Egypt, , 2009.11.Y. Sun, C. F. Babbs and E. J. Delp, “A comparisonof feature selection methods for the detection of breastcancers in mammograms: Adaptive sequential floatingsearch vs. genetic algorithm”, IEEE Intl. Conf., pp.6536 - 6539, 2005.12.A. K Mohanty and S. K Lenka, “Efficient imagemining technique for classification of mammograms todetect breast cancer”, IJCCT, vol. 2, Issue 2, 3, 4; pp.99-106, International Conference, 3rd -5th December2010.13.M. Virth, D. Nikitenko and J. Lyon, “Segmentationof the breast region in mammograms using arule-based fuzzy reasoning algorithm”, ICGST-GVIPJournal, vol. 5, Issue- 2, pp. 45-54, 2005.14.M. E. Cintra and M. C Monard, “An evaluation ofrule-based classification models induced by a fuzzymethod and two classic learning algorithms”, IEEEComputer Society, pp.188-193, 2010.15.Du Gen-Yuan, Miao Fang, Tian Sheng-li and LiuYe, “A modified C-means algorithm in remote sensingImage segmentation”, IEEE Intl. Conf., pp. 447-450,2009.



1

Fig.1: Flowchart.

Fig 2: Fis Editor file in MATLAB Fig. 3: Fuzzy rule base

Illustrations

Illustration 1

Figures



2

Fig. 4: Fuzzy rule viewer Fig 5: Fuzzy surface viewer

Fig 6: Fuzzy surface contour Fig 7: Screen of GUI Implementation in MATLAB



3

Fig 8: GUI Implementation of image (mdb001.jpg)

(a) Image mdb075 (b) Image mdb075




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Fig 9: GUI Implementation of various images.



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Mammograms Feature Extraction using Fuzzy Surface · Mammograms Feature Extraction using Fuzzy Surface. Author(s): Dubey R B, Nagpal A . Abstract. Mammography is the most contemporary