Available online at ScienceDirect. Procedia Computer Science 46 (2015 )

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1 Avalable onlne at ScenceDrect Proceda Computer Scence 46 (215 ) Internatonal Conference on Informaton and Communcaton Technologes (ICICT 214) Automatc Characterzaton of Bengn and Malgnant masses n Mammography K.Vadeh a,*, T.S.Subashn b a Research Scholar, b Assocate Professor, Department of Computer Scence and Engneerng,Faculty of Engneerng and Technology, Annamala Unversty, Inda Abstract The paper ams to develop an automated breast mass characterzaton system for assstng the radologst to analyze the dgtal mammograms. Mammographc Image Analyss Socety (MIAS) database mages are used n ths study. Fuzzy C-means technque s used to segment the mass regon from the nput mage. GLCM texture features namely contrast, correlaton, energy and homogenety are obtaned from the regon of nterest. The texture features extracted from gray level co-occurrence matrx (GLCM) are computed at dstance d=1 and θ=, 45, 9, 135. These wth three classfers namely adaboost, back propagaton neural network and sparse representaton classfers are used for characterzng the regon contanng ether bengn mass or malgnant mass. The expermental results show the SRC classfer s more effectve wth an accuracy of 93.75% and wth the Mathew s correlaton coeffcent (MCC) of 87.35% The Authors. Publshed by Elsever B.V. Ths s an open access artcle under the CC BY-NC-ND lcense ( Peer-revew under responsblty of organzng commttee of the Internatonal Conference on Informaton and Communcaton Peer-revew Technologes under (ICICT responsblty 214). of organzng commttee of the Internatonal Conference on Informaton and Communcaton Technologes (ICICT 214) Keywords:Bengn and malgnant mass classfcaton; Fuzzy C-Means clusterng; Sparse representaton classfer; Mathews correlaton coeffcent. * Correspondng Author. Tel.: ; E-mal address: vanakrshna@gmal.com The Authors. Publshed by Elsever B.V. Ths s an open access artcle under the CC BY-NC-ND lcense ( Peer-revew under responsblty of organzng commttee of the Internatonal Conference on Informaton and Communcaton Technologes (ICICT 214) do:1.116/j.procs

2 K. Vadeh and T.S. Subashn / Proceda Computer Scence 46 ( 215 ) Introducton Globally, breast cancer statstcs s alarmng and t s the second leadng fatal cancer n women next to cervcal cancer. In Inda, breast cancer cases are expected to double by 225. The Indan Cancer Socety has declared 213 as breast cancer awareness year and s takng varous ntatves to create awareness n people. Achevng bengn detecton and adequate treatment wll lead to better long term survval as well as a better qualty of lfe. Flm Mammography s the hghly used magng modalty for breast cancer detecton and dagnoss. The avalablty of dgtal mammograms today facltates the computer aded detecton and dagnoss of breast cancer as the breast mage could be easly stored n dgtal format drectly nto the computer memory.dgtal mammography process, gudelnes and advantages are vvdly explaned n 1. Mcro-calcfcatons and masses are two mportant early sgns of breast cancer. Masses are often obscured n the surroundng parenchymal tssue, so t s a challengng process to dstngush between the mass regon and normal breast regon. Abnormal growth of cells are consdered as mass whch s seen as hgh ntensty regons n an mammogram. Masses are consdered to bengn or malgnant where bengn masses are cancerous tumors and malgnant are non-cancerous tumors. Masses are found n several shapes namely crcumscrbed, speculated, ll-defned or lobulated. Masses wth more rregular shapes are malgnant and masses wth regular and smooth boundares are n bengn stage. For these reasons, texture measures have been proposed for dstngushng between bengn and malgnant masses. In ths paper GLCM and statstcal texture measures are ncorporated to extract the features. A novel sparse representaton classfcaton method s proposed to classfy the bengn and malgnant tumors n mammograms. The paper s organzed as follows. Secton 2 brefly revews some exstng technque for segmentaton and classfcaton of mammographc mass. Secton 3 descrbes the materals and proposed methodology for automatc characterzaton of breast mass. Secton 4 demonstrates the results and performance and fnally conclusons are presented n secton Lterature Revew Many researchers developed and revewed automatc mass detecton and classfcaton wth dfferent CAD approaches 2-5. Most of the exstng methods dffer n the types of features and classfers that have been used for bengn and malgnant classfcaton and the way the features have been extracted. Texture s an mportant characterstc that helps to dscrmnate and dentfy the objects. Texture descrptors have been used for detectng normal and abnormal leson regons n mammograms 6,7. The authors n 8 classfed the bengn and malgnant masses usng the shape based contnuous Zernke orthogonal moment and dscrete Krawtchouk orthogonal moment descrptors as features. The extracted feature dmensons are reduced usng PCA and K-nn classfer. The accuracy obtaned was 81% and 9.2% usng Zernke moment and Krawtchouk moment respectvely. From the four drectons of the gray level co-occurrence matrces, texture features such as Contrast, Energy and Homogenety features were extracted 9. These features classfed the mass nto ether speculated, ll-defned or crcumscrbed. Decson tree wth fve crtera wereanalyzed for classfcaton of masses. Morphologc multple concentrc layer analyss s used to dentfy mammographc masses 1. The detecton rate of ths CAD technque outperformed for dentfyng malgnant masses than bengn masses. Gabor flter bank at dfferent scales and orentatons s used to extract the texture features for characterzng the mass n mammography. Ths method acheved 94.92% and 85.53% accuracy wth SVM classfer for normal-mass classfcaton and bengnmalgnant classfcaton respectvely 11. Segmentaton approaches are classfed nto regon based methods 12, contour based methods 13, clusterng and thresholdng methods 14 and model based methods 15. In ths work, the clusterng based segmentaton technque s used. 3. Materals and methods The block dagram showng the varous steps of the proposed method s gven n Fg. 1.

3 1764 K. Vadeh and T.S. Subashn / Proceda Computer Scence 46 ( 215 ) Database descrpton An organzaton n UK called Mammography Image Analyss Socety (MIAS) has created a mammogram database. Ths database s used n ths study. It contans both the rght and left breast mages of the same patent wth a unform sze of 124 x 124 pxels. The mammogram n ths database s dgtzed to a resoluton of 5m x 5m, 8 bts represents each pxel. The database contans 322 mammograms of 161 patents, n whch 29 are normal mammograms and 113 are abnormal mammograms whch ncludes both mass and mcrocalcfcaton. The database has the ground truth detals such as tssue types, class of abnormalty, severty of abnormalty and locaton of abnormalty lke xy mage-coordnates of centre of abnormalty, and approxmate radus (n pxels) of a crcle enclosng the abnormalty 16. Segmentaon of mass regon from breast ROI Feature extracton Fuzzy C-means clusterng GLCM features Zernke moment features Classfcaton of bengn and malgnant mass Adaboost Back propogaton neural network (BPNN) Sparce representaton based classfer (SRC) 3.2. Mass regon Segmentaton Fg. 1. Block dagram showng the varous steps of the proposed method For computer aded detecton and dagnoss of breast mass, segmentng the mass regon accurately s the most vtal step. Breast masses are obscured by the normal breast parenchymal tssue. Fuzzy K-means clusterng based segmentaton approach s used n ths work. Fuzzy C-Means In the medcal doman, FCM s one of the most commonly used unsupervsed pattern recognton approach for tumor segmentaton. In 1981,As an alternatve to C-means clusterng algorthm Bezdekformulated the Fuzzy C- means algorthm 17. FCM employs fuzzy parttonng, ths approach parttons the data ponts nto K clusters and the degree of assocaton of a data pont to each cluster s denoted by the membershp grades rangng between and 1. In hard C-means algorthm, each group of data ponts n a dataset completely belongs to one cluster. Whereas, Fuzzy C-means algorthm allows data ponts nearer to the cluster center have a hgh degree of belongng and those that are far away from the cluster center have a less degree of assocaton wth ts cluster centers. Algorthm for Fuzzy C-Means clusterng: The goal of Fuzzy c-means s to mnmze the followng objectve functon of weghted dstances of the data to the centers. O m C N v 1 j1 m j x j c 2 (1)

4 K. Vadeh and T.S. Subashn / Proceda Computer Scence 46 ( 215 ) where m>1, the degree of membershp of X j n the cluster C s gven by V j,c s the th cluster of the n- dmensonal centerand x j s the th data pont of the n-dmensonal data. The membershp matrx V=[vj] have elements wth the values rangng between and 1. The objectve functon (Om) s optmzed wth an teratve functon to acheve fuzzy parttonng and the elements of the membershp matrx and the cluster center s updated and s gven by : In medcal clusterng, the pxel ntensty varaton s very useful to segment the tumor regon and normal regon. When the hgh membershp values are assgned to the pxel ntenstes whch are close to a cluster center and low membershp values are assgned to the pxel ntenstes whch are far away from the cluster center, then the objectve functon s mnmzed. In ths work Fuzzy K-means algorthm s appled to segment the mass regon Feature Extracton Texture characterstcs gve more sgnfcant nformaton n pattern recognton area and n ths work and for mass classfcaton GLCM features are derved from the mass regon. GLCM s one of the wdely used technques for texture analyss. Four texture descrptors namely contrast, correlaton, energy and homogenetyused for classfcaton of bengn and malgnant masses are computed at dfferent orentatons wth the dstance of 1 pxel. Intenstycontrast between a current pxel and ts neghbour n an mage s gven by the contrast descrptor. Correlaton tells how current pxel and ts neghbourhood pxel s related to one another. Energy s the angular second moment. Homogenety gves the dea about the closeness of the dstrbuton of GLCM elements to ts dagonal 3.4. Classfcaton Ada Boost vj k 1 c c 2 ( m1) In 23, Yoav Freund and Robert Schaphre formulated a machne learnng meta-algorthm called Adaptve Boostng (AdaBoost). It s very smple to mplement and good for generalzaton. It mproves the classfcaton accuracy and not prone to over fttng. It s an teratve algorthm and durng an each teraton of the tranng phase, a new weak learner s added to create a strong learner that s only slghtly correlated to the classfer. The weghtng vector s adjusted every tme the weak learner s added to the ensemble to focus on examples that were msclassfed n the earler teraton. Hence t s called adaptve and fnally results wth a classfer wth better accuracy. Back Propogaton Neural Network (BPNN) N x x A BPNN s a supervsed machne learnng technque and uses a feed-forward archtecture. The BPNN s based on the gradent descent technque for solvng an optmzaton problem, whch nvolves the mnmzaton of the network cumulatve error. Error s the dfference between the target output and the actual output. Ths BPNN s desgned n such a way as to update the weghts n the drecton of the gradent descent of the cumulatve error. Ths s done n an teratve way. The weghts are adjusted durng each teraton by propagatng the errors backwards. Ths s contnued untl the mean square error s mnmzed to an acceptable level 18. The Sparse Representaton based classfer (SRC) j j 1 k C N m v x j j j1 N m v j1 In ths work for mass classfcaton SRC s used. In SRC, the gven test sample can be represented as a lnear j (2)

5 1766 K. Vadeh and T.S. Subashn / Proceda Computer Scence 46 ( 215 ) combnaton of the tranng sample and does not requre any formal tranng process 19, 2. Let A be the tranng sample matrx of p classes. A A A,... A { v, v,... v n } 1, 2 p 1 12 (3) A test mammaogram mage y can be well approxmated as a lnear combnaton of the tranng sample taken from A and s gven by n y a V j 1 j j (4) wherenrepresents the total samples n the th class. Now eqn. (4) can be rewrtten as y=ax (5) Snce A s the dctonary contanng all the tranng samples. x,,...,...,,1,,2,..., n T s the coeffcent vector where only a few coeffcents are non- zero whereas others are zero. The nonzero coeffcents are alone related to class. Thus sparse soluton of the coeffcent vector whch s same as solvng the followng optmzaton problem (l mnmzaton) could be evaluated as gven n equaton (6). xˆ arg mn x subject to Ax=y (6) Because the lack of l norm s mathematcal representaton, l mnmzaton s regarded as an NP-hard problem, as t s too complex and almost mpossble to solve. In many cases, l mnmzaton problem s relaxed to be hgher order norm problem such as l 1 mnmzaton and l 2 mnmzaton. The SRC approxmated the l norm coeffcents by l 1 mnmzaton problem 21. If the test sample y belongs to a certan class, the coeffcents n the estmated ˆx not wthn ths class should all be zeros. But gven the nose and the modellng error, the nosy model s modfed as: y=ax +ε (7) whereε s the nose level. Then, the equaton (6) s converted nto: xˆ arg mn x subject to Ax y 2 (8) We consder usng the resdual error to classfy y. After estmatng ˆx 1, the gven test mass mage ŷ s approxmated as: yˆ A ( xˆ ) where ( xˆ ) s a new vector. The nonzero entres n ˆx 1 are alone related to class. The resdual error r yˆ y A xˆ r (9) y ˆ s: (1) The test sample y s classfed wth the class havng the mnmal resdual error. The SRC algorthm has good

6 K. Vadeh and T.S. Subashn / Proceda Computer Scence 46 ( 215 ) generalzaton ablty. So t s more sutable for medcal applcatons Performance measures Table 1 presents a confuson matrx for bnary classfcaton, where Arepresents true postve, Brepresents false postve, Crepresents false negatve, and Drepresents true negatve counts. Accuracy, senstvty, specfcty, PPV, NPV, MCC are some of the measures used for evaluatng the performance of the classfers used n ths work namely adaboost, SRC 22. Table 1. Confuson matrx for mass classfcaton Class/Recognsed Bengn Malgnant Bengn True Postve (A) False Negatve (C) Malgnant False Postve (B) True Negatve (D) A D Accuracy A B C D A Senstvt y A C D Specfcty B D A PPV A B D NPV D C A D B C MCC ( A B)( A C)( D B)( D C) Mathews Correlaton Coeffcent (MCC) s another accuracy evaluaton measure whch could gve a better pcture of the performance of the classfer. MCC s used as a metrc rather than accuracy when the number of samples n the two classes s unbalanced. 4. Expermental Results The MIAS database contans ground truth of the mage whch ncludes the center of the mass and approxmate radus of the crcle enclosng the mass n terms of number of pxels. Totally 48 abnormal mammograms contanng mass s consdered for ths study. Out of whch 16 are malgnant mammograms and 32 are bengn mammograms. A square regon of area 174x174 pxels s taken as the ROI for further processng. The value 174 s chosen n consultaton wth the radologst because t s the radus of the largest mass present n the database and moreover mass wll not be more than the sze of 174x174. Fg.2. shows the segmented nput breast regon. Fg x 174 sze segmented nput breast regon From the nput ROI thus obtaned whch s shown n Fg. 3a., the mass regon s segmented usng fuzzy K-means clusterng algorthm and Fg.3b. shows the segmented mass regon. To obtan the contour of the mass mage, morphologcal eroson appled mage s subtracted from the orgnal mage whch s nothng but the gradent of the orgnal mage. The contour thus obtaned s shown n Fg. 3c. Then morphologcal operaton areaopen s appled, to retan only those regons whch contan more than 6 pxels and ths removes smaller regons. Fg.3d. shows the mass regon alone wthout smaller regons. Then the orgnal mage s compared wth Fg.3d and all the pxels whch

7 1768 K. Vadeh and T.S. Subashn / Proceda Computer Scence 46 ( 215 ) s whte s replaced wth the orgnal ntensty values to obtan the segmented mass and t s shown n Fg. 3e. Fg. 3. a) nput ROI b) FCM segmented mass mage c) contour of mass d) after applyng morphologcal operaton e) orgnal pxel values of the segmented mass. Four features derved from the GLCM (contrast, correlaton, energy and homogenety) are calculated at θ =, 45, 9, 135 and d=1, Snce the relable nformaton cannot be gven by a sngle drecton.therefore, 4drectons from the co-occurrence matrx are used for extractng the second order texture nformaton from the mammograms. The derved features are fed nto the classfers namely Adaboost, SRC and BPNN. Table 2. Performance measures of dfferent classfers wth local bnary pattern texture features Type Accuracy (%) Senstvty (%) Specfcty (%) PPV (%) NPV (%) MCC (%) Adaboost BPNN SRC The performance measures used for classfcaton are accuracy, senstvty, specfcty, PPV, NPV and MCC. Leave one out procedure has been adopted n testng the performance of the varous classfers. Table 2 shows the performance of the three classfers wth GLCM features. It could be seen that the SRC outperformed Adaboost and BPNN n classfyng bengn and malgnant masses. The accuracy obtaned was 93.75%. Mathews Correlaton Coeffcent (MCC) s calculated to get a better pcture of the performance of the classfer, snce the number of samples n the two classes s unbalanced. When compared to accuracy MCC s used n cases where the number of samples n each of the classes dffers consderably. SRC obtaned the hghest MCC of 87.35% whereas the other classfers reported relatvely poor MCC. 5. Conclusons In ths paper, t s proposed an automatc system whch detects and categorzes bengn mass and malgnant mass regons from the breast ROI. Mn-mas database mages are used for ths study. Ths automatc detecton of masses s benefcal to the radologst for fndng the early stage of (bengn) mass and cancerous stage (malgnant) wthout confuson. Even though the mass regons are obscured n the dense regons, the study reveals the usefulness of fuzzy C-means algorthm for segmentng mass regons from the ROI. The expermental results show that the extracted GLCM descrptors along wth SRC classfer could be effectvely used n classfyng breast masses n dgtal mammograms. The SRC classfer obtaned hghest accuracy s 93.75%. The proposed work was carred out usng MATLAB 212a. Acknowledgement The fnancal support receved from UGC to carry out ths work under UGC major research project s hghly acknowledged.

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