Combination of Novel Enhancement Technique and Fuzzy C Means Clustering Technique in Breast Cancer Detection.

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1 Biomedical Reearch 2013; 24 (2); ISSN X Combination of Novel Enhancement Technique and Fuzz C Mean Clutering Technique in Breat Cancer Detection. B. Senthilkumar 1 and G.Umamahewari 2 1 Department of Electronic and Communication Engineering, Tamilnadu College of Engineering, Coimbatore, Tamilnadu, India Department of Electronic and Communication Engineering, P.S.G College of Technolog, Coimbatore, Tamilnadu, India en17580@ahoo.co.in Abtract Computer aided detection (CAD) i the main aid ued b radiologit in detecting microcalcification in digital mammogram for the earl detection of breat cancer. In thi paper we have improved the preproceing method involve in CAD b modifing the local range modification (LRM) a modified LRM (MLRM) for the noie removal and enhancement. And we have combined thi method with the fuzz C mean clutering (FCMC) method and teted for over 30 mammogram image and found the microcalcification detection accurac of 98.1 % which i better than the other eiting method. Keword. CAD, Preproceing, Breat cancer, Mammogram, Microcalcification, MLRM, FCMC. Accepted Januar Introduction Breat cancer i a major public health problem in the world and the mot common form of cancer among women worldwide. It currentl account for more than 30% of cancer incidence and a ignificant % of cancer mortalit in both developing and developed countrie. Succeful treatment relie on earl detection [1]. Digital mammograph i one of the mot reliable method involve in the detection and diagnoi of breat pathological diorder [2]. Analzing mammogram image i the mot challenging tak in medical image proceing. Computer aided detection (CAD) tool i the aid for the radiologit in analzing uch image for the effective detection and diagnoi of the dieae. Such a CAD tool conit of Preproceing, Segmentation and detection procee [2]. Dene region in digital mammogram image are uuall noi and have low contrat and their viual creening i difficult [3]. Contrat enhancement i the mot enitive imaging technique for breat cancer detection. Global and local hitogram equalization technique had been propoed b [4]. ACM active contour model [5], patial contraint to a fuzz cluter [6], Markov random field (MRF) [7] had been propoed for the preproceing. Image i modeled a a et of patial pattern to incorporate the patial information implied b each pattern into the object function of fuzz C mean (FCM) clutering, in [8], preented a new method of diimilarit between a patial pattern and a cluter, which reflect not onl the ditance in feature pace, location of the pattern of the lattice. Feature etraction i ued to find an appropriate meaure to characterize the homogeneit of each region inide an image [8]. The contrat in mammogram i ver low and the boundar between normal tiue and tumor i unclear, the traditional egmentation method might not work well [4]. Image enhancement algorithm ha been utilized for the improvement of contrat feature and the uppreion of noie [2]. Contrat limited adaptive hitogram equalization (CLAHE) baed on local parameter wa propoed b [9], region baed approach for the enhancement of region of interet (ROI) ha been propoed b [10]. Non linear gra level re-caling method ha been ued for enhancement [3] and filtering ignal dependent noie on digitized mammographic phantom image uing a direct contrat modification method wa propoed b [11]. Automated interpretation of microcalcification and mae are ver difficult ince the ROI are uuall of low contrat, epeciall in the age of oung women [12]. So, Mammographic feature enhancement (cluter detection and enhancement) will be eential and critical for automated mammogram anali. It i performed b emphaizing image feature and uppreing noie o that the image qualit can be greatl improved and be ueful for breat cancer diagnoi. In thi paper we have di- Biomed Re-India 2013 Volume 24 Iue 2 252

2 Combination of Novel Enhancement Technique and Fuzz C Mean Clutering.. cued about the MLRM for noie removal and contrat enhancement and FCMC for cluter detection and enhancement (FCMC). Material and Method The databae of mammogram ued in thi work i known a Mammographic Image Anali Societ (MIAS) Mini Mammographic Databae. The eample image ued in thi paper i mdb75 and it i hown a original image in figure 1(a). The entire method preented in thi paper wa implemented in MATLAB 7.0, and make etenive ue of the Image Proceing Toolbo. The methodolog ued conit of two main tage. Firt i the pre-proceing (MLRM) tage and it conit of noie removal and enhancement. Second i the egmentation tage (FCMC cluter detection and enhancement). Modified Local Range Modification (MLRM) Method The MLRM algorithm procee ame a that of LRM [2] but with two change. The firt i maimum and minimum piel value of non-overlapping 4848 piel ized block are computed during firt pa intead of 5151 in LRM. And the econd i etimation of regional maimum and minimum value baed on the interpolation of eight urrounding grid point (hown in figure 1 a bold letter) intead of four in LRM [2] i hown in figure 1. (H 1, L 1 ) (H 2, L 2 ) (H 3, L 3 ) (M 1, N 1 ) (M 2, N 2 ) (M 3, N 3 ) (H 4, L 4 ) (H 5, L 5 ) (H 6, L 6 ) (M 4, N 4 ) (M 5, N 5 ) (min, ma) (M 6, N 6 ) (H 7, L 7 ) (H 8, L 8 ) (H 9, L 9 ) (M 7, N 7 ) (M 8, N 8 ) (M 9, N 9 ) Figure 1. Etimation of regional maimum and minimum value baed on the interpolation of eight urrounding grid point ((H i, L i ) and (M i, N i ) are the minimum and maimum gracale value for each grid point ). The (H i, L i ) and (M i, N i ) are the minimum and maimum gracale value. ma = M M M 2 M (1) from (1), i the ize of the block, and are the horizontal and vertical ditance of the eamined point, repectivel, from the M 5 grid point, and M 1, M 2, M 3, M 4, M 6, M 7, M 8 and M 9 are the intenit value of the eight urrounding grid point. Thee modification enhance the image better than the other method. The output value of each piel with coordinate [m, n] i calculated b linear tretching given in (2). L 1, = ( n min), (2) [ m n] ( ma min) [ m, ] Where L i the number of gracale (image depth), ma and min are the margin of the local input gracale range, repectivel. Thee modification enhance the image better and hown in figure 2(b). Biomed Re-India 2013 Volume 24 Iue 2 253

3 Senthilkumar/Umamahewari (a) Original Image (b) MLRM Enhanced Image Figure 2. MLRM Enhanced Image Output of mdb75, In figure 2, 2(a) i the original image and 2(b) i the MLRM enhanced image. Fuzz C Mean Clutering (FCMC) Proce of grouping the object into cluter i the main work of FCMC. Tree tructured non linear filter i ued in [13] for the enhancement and egmentation of microcalcification cluter. Radiologit uuall ue cluter to claif the true poitive (TP) and fale poitive (FP) calcification [14]. Object within a given cluter have a high degree of imilarit and object belonging to different cluter have a high degree of diimilarit. Baed on thee criteria FCMC grouping the feature in different categor a cluter. Objective function J of FCMC i given below in (3). J c N = i= 1 j= 1 m Uij j Ci, 1 m α (3) where m i an real number greater than 1, c i number of cluter, X i the i object of given N object, U i the degree of memberhip of in the cluter j, C i centroid of cluter j, i euclidean ditance between an data object and the centroid. The parameter m ( 1) i called fuzzifier and ignifie the amount of fuzzine in the olution et. And the algorithm i a follow. Input : Dataet X of n object with d feature, value of K and fuzzification value m>1 Output : Memberhip matri U for n object and K cluter Step-1: Declare a memberhip matri U of ize nk. Step-2: Generate K cluter centroid randoml within the range of the data or elect K object randoml a initial cluter centroid. Let the centroid be c, c,, c. Step-3: Calculate the ditance meaure d= -c uing Euclidean ditance, for all cluter centroid and data object,i=1,2,,n. Step-4: Compute the Fuzz memberhip matri U uing (3) Step-5: Compute new cluter centroid c, Step-6: Repeat tep 3 to 5 until convergence. Reult and Dicuion The propoed combination detect the cancer in an effective wa and the reulted image are given in figure 2. Thi mammogram image ha been teted alread and found that it i a fatt breat with malignanc. In figure 3, 3(a) ROI from the original image mdb75, 3(b) Image labeled b cluter indeed image, 3(c) Object in cluter 1, 3(d) Object in cluter 2, 3(e) Object in cluter 3 and 3(f) Object in cluter 4. Thu the FCMC method perform well on mammogram image, it egmented and enhanced the required area in the mammogram called upiciou region. Detection of microcalcification and ma were done baed on the above aid cluter detection and the election of upiciou region i performed baed on the ranking tem in [15]. The comparion of propoed with the other eiting method i given in table Biomed Re-India 2013 Volume 24 Iue 2

4 (a) Region of Interet (ROI) (b) Image Labeled b Cluter Inde (c) Object in Cluter 1 (d) Object in Cluter 2 (e) Object in Cluter 3 (f) Object in Cluter 4 Figure 3. FCMC Output of mdb75 Table 1. Comparion of Variou Enhancement Technique with the MLRM. Percentage of the Enhancement technique mot contrated piel (%) CLAHE LRM Modified LRM (MLRM) WLST WSRK WBGK Without Enhancement ROC MLRM (0.981) LRM (0.865) WLST (0.862) WBGK (0.823) WSRK (0.815) CLAHE (0.807) WOE (0.803) TPF FPF Figure 4 ROC Anali Further, the receiver operating characteritic (ROC) anali ha been done for the method in table 1. From figure 4 the average ROC value of LRM and MLRM ha been compared with Contrat Limited Adaptive Hitogram Equalization (CLAHE), Wavelet Linear Stretching (WLST), Wavelet Shrinkage (WSRK), Wavelet Background Approimation (WBGK) and without enhancement (WOE). Biomed Re-India 2013 Volume 24 Iue 2 255

5 Senthilkumar/Umamahewari Concluion Thi new method provide good upport to the radiologit in detecting the breat cancer. The mall modification in the LRM technique reduce the noie and enhance the mammogram image. And MLRM ue eight urrounding piel intead of four in LRM, thi made the propoed method little bit comple and becaue of thi the computational compleit ma increae. But the image qualit found i good and atifactor when comparing to LRM. Further the FCMC technique i ued to correctl egment and enhance the cluter. Thi combination (MLRM and FCMC) provide a good platform in detecting breat cancer with the accurac of 98.1% and ha alo been validated b epert radiologit Reference 1. Padaachee J, Alport MJ, Rae WID. Identification of the breat edge uing area encloed b io-intenit contour. Computerized Medical Imaging and Graphic 2007; 31: Papadopoulo A, Fotiadi DI, Cotaridou L. Improvement of microcalcification cluter detection in mammograph utilizing image enhancement technique. Computer in Biolog and Medicine 2008; 38: Scharcanki J, Jung CR. Denoiing and enhancing digital mammographic image for viual creening. Computerized Medical Imaging and Graphic 2006; 30: Cheng HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN. Approache for Automated Detection and Claification of Mae in Mammogram. Pattern Recognition 2006; 39: Precioo FM, Blu Barlaud T, Uner M. Robut realtime egmentation of image and video uing a mooth-pine nake-baed algorithm. IEEE Tranaction on Image Proceing 2005; 14: Liew AWC, Leung SH, Lau WH. Segmentation of colour lip image b patial fuzz clutering. IEEE Tranaction on Fuzz Stem 2003; 11: Deng H, Claui DA. Unupervied egmentation of nthetic aperture radar ea ice imager uing a novel Markov random field model. IEEE Tranaction on Geocience and Remote Sening 2005; 43: Youg Xia, (David) Dagan Feng, Tianjiao Wang, Rongchun Zhao, Yanning Zhang. Image egmentation b clutering of patial pattern. Pattern Recognition Letter 2007; 28: Pizer SM, Amburn EOP, Autin JD. Adaptive hitogram equalization and it variation. Computer Viion, Graphic and Image Proceing 1987; 39: Morrow WM, Paranjape RB, Rangaan RM. Regionbaed contrat enhancement of mammogram. IEEE Tranaction on Medical Imaging 1992; 11: Adel M, Zuwala D, Raigni M, Bourennane S. Filtering noie on mammographic phantom image uing local contrat modification function. Image and Viion Computing 2008; 26: Cheng HD, Huijuan Xu. A novel fuzz logic approach to mammogram contrat enhancement. Information Science 2002; 148: Qian W. Tree-tructured non-linear filter in digital mammograph. IEEE Tranaction on Medical Imaging 1994; 13: Zhang L, Shankar R, Qian W. Advance in micro calcification cluter detection in mammograph. Computer in Biolog and Medicine 2002; 32: Dominguez AR, Nandi AK. Detection of mae in mammogram via tatiticall baed enhancement, multilevel-threholding egmentation and region election. Computerized Medical Imaging and Graphic 2008; 32: Correpondence to: B. Senthilkumar Department of Electronic and Communication Engineering Tamilnadu College of Engineering Coimbatore , Tamilnadu, India. 256 Biomed Re-India 2013 Volume 24 Iue 2

6 Combination of Novel Enhancement Technique and Fuzz C Mean Clutering.. Biomed Re-India 2013 Volume 24 Iue 2 257