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Support Vector Machine Approach for Classification of Cancerous Prostate Regions

Authors: Metehan Makinacı


The objective of this paper, is to apply support vector machine (SVM) approach for the classification of cancerous and normal regions of prostate images. Three kinds of textural features are extracted and used for the analysis: parameters of the Gauss- Markov random field (GMRF), correlation function and relative entropy. Prostate images are acquired by the system consisting of a microscope, video camera and a digitizing board. Cross-validated classification over a database of 46 images is implemented to evaluate the performance. In SVM classification, sensitivity and specificity of 96.2% and 97.0% are achieved for the 32x32 pixel block sized data, respectively, with an overall accuracy of 96.6%. Classification performance is compared with artificial neural network and k-nearest neighbor classifiers. Experimental results demonstrate that the SVM approach gives the best performance.

Keywords: Support Vector Machines, Computer-Aided Diagnosis, texture classification, Gauss-Markov random fields

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[1] U. Schenck, and W. Planding, "Quantitation of visual screening technique in cytology," in Proc. Image Analysis in Medicine, II. National Symposium, pp. 7-14, 1998.
[2] D. Kopec, M.H. Kabir, D. Reinharth, O. Rothschild, and J.A. Castiglione, "Human Errors in Medical Practice: Systematic Classification and Reduction with Automated Information Systems," J. of Medical Systems, vol. 27, no. 4, pp. 297-313, Aug. 2003.
[3] Vapnik, V., Statistical Learning Theory, Wiley, New York, 1998.
[4] I. El-Naqa, Y. Yang, M.N. Wernick, N.P. Galatsanos, R.M. Nishikawa, "A Support Vector Machine Approach for Detection of Microcalcifications", IEEE Tran. Med. Imaging, vol. 21, no. 12, pp. 1552-1563, Dec. 2002.
[5] A. Schwaighofer, P. Mayer, A. Krause, J. Beuthan, H. Rost, G. Metzger, G.A. M├╝ller, A.K. Scheel, "Classification Of Rheumatoid Inflammation Based on Laser Imaging", IEEE Tran. on Biomed. Eng., vol. 50, no. 3, pp. 375-382, March 2003.
[6] K.N. Prakash, A.G. Ramakrishnan, S. Suresh, T.W.P. Chow, "Fetal Lung Maturity Analysis Using Ultrasound Inage Features", IEEE Tran. on Inf. Tech. in Biomed., vol. 6, no. 1, pp. 38-45, March 2002.
[7] S.B. Gökt├╝rk, C. Tomasi, B. Acar, C.F. Beaulieu, D.S. Paik, R.B. Jeffrey, J. Yee, S. Napel, "A Statistical 3-D Pattern Processing Method for Computer-Aided Detection of Polyps in CT Colonography", IEEE Tran. on Med. Imag., vol. 20, no. 12, pp. 1251-1260, Dec. 2001.
[8] S. Chatterjee, "Classification of natural textures using Gaussian Markov random field models", in Markov Random Fields, Theory and Applications, R. Chellappa, A. Jain, Ed., Academic Press, 1993, pp. 159-177.
[9] B.S. Manjunath, R. Chellappa, "Unsupervised texture segmentation using Markov random field models," IEEE Tran. Patt. Anal. Machine Intel., vol. 13, pp. 478-482, 1991.
[10] M.E. Jernigan, F. D-Astous, "Entropy-based texture analysis in the spatial frequency domain," IEEE Tran. Patt. Anal. Machine Intel., vol. 6, pp. 237-243, 1984.
[11] S.Z. Li, Markov Random Field Modelling in Computer Vision, Springer- Verlag, 1995.
[12] A. Rosenfeld, "Image modelling during the 1980s: A brief overview", in Markov Random Fields, Theory and Applications, R. Chellappa, A. Jain, Ed., Academic Press, 1993, pp. 1-10.
[13] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd ed., John Wiley & Sons, Inc., 2001, ch. 2,4,6.