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Computer Aided Classification of Architectural Distortion in Mammograms Using Texture Features

Authors: Birmohan Singh, V. K. Jain


Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Architectural distortions, masses and microcalcifications are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support vector machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and an accuracy of 96% for mammogram images collected from digital database for screening mammography database.

Keywords: support vector machine, architecture distortion, mammograms, GLCM texture features, GLRLM texture features

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[1] T. Ichikawa, T. Matsubara, T. Hara, H. Fujita, T. Endo, and T. Iwase, “Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis”, In Medical Imaging 2004, International Society for Optics and Photonics, May 2004, pp.920-925.
[2] X. Zhang, N. Homma, S. Goto, Y. Kawasumi, T. Ishibashi, M. Abe, N. Sugita, and M. Yoshizawa, “A hybrid image filtering method for computer-aided detection of microcalcification clusters in mammogram”, Journal of Medical Engineering, vol.2013, Article ID 615254, pp.1-8, 2013.
[3] J. Tang, R.M. Rangayyan, J. Xu, I. El Naqa, and Y. Yang, “Computeraided detection and diagnosis of breast cancer with mammography: recent advances”, IEEE Transactions on Information Technology in Biomedicine, vol.13(2), pp.236-251, 2009.
[4] Q. Guo, J. Shao, and V. Ruiz, “Investigation of support vector machine for the detection of architectural distortion in mammographic images”, Journal of Physics: Conference Series, vol.15, pp.88–94, 2005.
[5] M.P. Sampat, G.J. Whitman, M.K. Markey, and A.C. Bovik, “Evidence based detection of spiculated masses and architectural distortion”, SPIE medical imaging 2005: Image Processing, San Diego, vol. 5747, 2005, pp.26-37.
[6] R. Nakayama, R. Watanabe, T. Kawamura, T. Takada, K. Yamamoto and K. Takeda, “Computer aided diagnosis scheme for detection of architectural distortion on mammograms using multiresolution analysis”, International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2008), vol.3(1), 2008, pp. S418–S419.
[7] R.M. Rangayyan, S. Banik, and J.L. Desautels, “Computer-aided detection of architectural distortion in prior mammograms of interval cancer”, Journal of Digital Imaging, vol.23(5), pp.611-631, 2010.
[8] S.K. Biswas, and D.P. Mukherjee, “Recognizing architectural distortion in mammogram: a multiscale texture modeling approach with GMM”, IEEE Transactions on Biomedical Engineering, vol.58(7), pp.2023-2030, 2011.
[9] T. Handa, X. Zhang, N. Homma, T. Ishibashi, Y. Kawasumi, M. Abe, and M. Yoshizawa, “DoG-based detection of architectural distortion in mammographic images for computer-aided detection”, In SICE Annual Conference (SICE), 2012 Proceedings of (pp. 762-767). IEEE., August 2012.
[10] A. Kamra, S. Singh, and V.K. Jain, “Towards the detection of architecture distortion in mammograms: a review”, International Journal of Computer Applications, vol.46(7), pp.44-49, 2012.
[11] A.C. Phadke, and P.P. Rege, “Classification of architectural distortion from other abnormalities in mammograms”, International Journal of Application or Innovation in Engineering & Management, vol.2(2), pp.42-48, 2013.
[12] R. Yoshikawa, A. Teramoto, T. Matsubara, and H. Fujita, “Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter”, In SPIE Medical Imaging (pp. 86701Z-86701Z). International Society for Optics and Photonics. Vol.8670, March 2013.
[13] A. Bailur, A. K. Pandey, A. K. Sharma, S. Saseendran, and Abhinav, “Modified gabor filter with control chart and image plots for identifying architectural distortion of mammogram images”, International Journal of Computer Science and Telecommunications, vol.5(4), pp. 12-19, 2014.
[14] M. Heath, K. Bowyer, D. Kopans, R. Moore, P. Kegelmeyer, “The digital database for screening mammography”, In 5th international workshop on digital mammography (pp. 212-218). June 2000.
[15] E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms”, Journal of Digital Imaging, vol. 11(4), pp.193-200, 1998.
[16] P. Filipczuk, T. Fevens, A. Krzyzak, and A. Obuchowicz, “GLCM and GLRLM based texture features for computer-aided breast cancer diagnosis”, Journal of Medical Informatics & Technologies, vol.19, pp.109-116, 2012.
[17] P.M. de Sousa Carvalho, A.C. de Paiva, and A.C. Silva, “Classification of breast tissues in mammographic images in mass and non-mass using Mcintosh’s diversity index and SVM”. In Machine Learning and Data Mining in Pattern Recognition, Springer Berlin Heidelberg, pp. 482- 494, 2012.
[18] A.K. Mohanty, M.R. Senapati, S. Beberta, and S.K. Lenka, “Texturebased features for classification of mammograms using decision tree”, Neural Computing and Applications, vol.23(3:4), pp.1011-1017, 2013.
[19] R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features of image classification”, IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-3(6), Nov. 1973.
[20] L. Soh and C. Tsatsoulis, “Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices”, IEEE Transactions on Geoscience and Remote Sensing, vol.37(2), 1999.
[21] M. Galloway, “Texture analysis using gray level run lengths”, Computer Graphics and Image Processing, vol.4(2), pp.172-179, 1975.
[22] R.C. Gonzalez, R.E. Woods, and S.L. Eddins, “Digital image processing using Matlab”, Pearson Education, India, pp.468-469, 2004.
[23] A.F. Costa, G. Humpire-Mamani, and A.J.M. Traina, “An efficient algorithm for fractal analysis of textures”, In 25th Conference on Graphics, Patterns and Images (SIBGRAPI 2012), Ouro Preto, 2012, pp. 39-46.
[24] L.O. Martins, G.B. Junior, A.C. Silva, A.C. Paiva, and M. Gattass, “Detection of masses in digital mammograms using K-means and support vector machine”, Electronic Letter on Computer Vision and Image Analysis, vol.8(2), pp.39-50, 2009.
[25] A. Papadopoulos, D.I. Fotiadis, and A. Likas, “Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines”, Artificial Intelligence in Medicine, vol.34(2), pp.141-150, 2005.
[26] P. Gorgel, A. Sertbas, N. Kilic, and O.N. Ucan, “Mammographical mass detection and classification using local seed region growing-spherical wavelet transform hybrid scheme”, Computers in Biology and Medicine, vol.43(6), pp.765-774, 2013.