Abstract
Prostatic biopsies provide the information for the determined diagnosis of prostatic cancer. Computer-aid investigation of biopsies can reduce the loading of pathologists and also inter- and intra-observer variability as well. In this paper, we proposed a two stages approach for prostatic cancer grading according to Gleason grading system. The first stage classifies biopsy images into clusters based on their Skeleton-set (SK-set), so that images in the same cluster consist of the similar two-tone texture. In the second stage, we analyzed the fractal dimension of sub-bands derived from the images of prostatic biopsies. We adopted the Support Vector Machines as the classifier and using the leaving-one-out approach to estimate error rate. The present experimental results demonstrated that 92.1% of accuracy for a set of 1000 pathological prostatic biopsy images.
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References
American Cancer Society, Cancer Facts & Figures 2007. American Cancer Society, Atlanta, GA (2007)
Balk, S.P., Ko, Y.J., Bubley, G.J.: Biology of prostate-specific antigen. L. Clin. Oncol. 21(2), 383–391 (2003)
O’Dowd, G.J., Veltri, R.W., Miller, M.C., Strum, S.B.: The Gleason Score: A Significant Biologic Manifestation of Prostatic cancer Aggressiveness on Biopsy. Prostatic Cancer Research Institute: PCRI Insights 4(1), 1–5 (2001)
Stotzka, R., Männer, R., Bartels, P.H., Thompson, D.: A hybrid neural and statistical classifier system for histopathologic grading of prostate lesions. Analytical Quantitative Cytol. Histol. 17(3), 204–218 (1995)
Tabesh, A., Kumar, V., Pang, H., Verbel, D., Kotsianti, A., Teverovskiy, M., Saidi, O.: Automated prostatic cancer diagnosis and gleason grading of tissue microarrays. In: Proceedings of the SPIE on Medical Imaging, vol. 5747, pp. 58–70 (April 2005)
Wetzel, A.W., Crowley, R., Kim, S.J., Dawson, R., Zheng, L., Joo, Y.M., Yagi, Y., Gilbertson, J., Gadd, C., Deerfield, D.W., Becich, M.J.: Evaluation of prostate tumor grades by content-based image retrieval. In: Proceedings of the SPIE on AIPR Workshop Advances in Computer-Assisted Recognition, Washington, DC, vol. 3584, pp. 244–252 (1999)
Smith, Y., Zajicek, G., Werman, M., Pizov, G., Sherman, Y.: Similarity measurement method for the classification of architecturally differentiated images. Computers and Biomedical Research 32(1), 1–12 (1999)
Jafari-Khouzani, K., Soltanian-Zadeh, H.: Multiwavelet grading of pathological images of prostate. IEEE Transactions on Biomedical Engineering 50(6), 697–704 (2003)
Huang, W., Lee, C.-H.: Automatic Classification for Pathological Prostate Images Based on Fractal Analysis. IEEE Trans. Medical Imaging 28(7) (July 2009)
Haykin, S.: 9. Self-organizing maps. In: Neural networks - A comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999) ISBN 0-13-908385-5
Mandelbrot, B.B.: Fractal Geometry of Nature. Freeman, San Francisco (1982)
Sarkar, N., Chaudhuri, B.B.: An efficient differential box-counting approach to compute fractal dimension of image. IEEE Transactions on Systems, Man and Cybernetics 24(1), 115–120 (1994)
Chaudhuri, B.B., Sarkar, N.: Texture segmentation using fractal dimension. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(1) (1995)
Voss, R.F.: Random fractals: Characterization and measurement. In: Pynn, R., Skjeltorp, A. (eds.) Scaling Phenomena in Disorderd Systems. Plenum, New York (1986)
Baish, J.W., Jain, P.K.: Fractals and cancer. Cancer Research 60, 3683–3688 (2000)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, New Jersey (2002)
Gose, E., Johnsonbaugh, R., Jost, S.: Pattern Recognition and Image Analysis. Prentice-Hall, Englewood Cliffs (1996)
Chui, C.K., Lian, J.A.: A study of orthonormal multiwavelets. Appl. Numer. Math. 20, 273–298 (1995)
Meyer, D., Leisch, F., Hornik, K.: The support vector machine under test. Neurocomputing 55(1-2), 169–186 (2003)
Pudil, P., Ferri, F.J., Novovičová, J., Kittler, J.: Floating Search Methods for Feature Selection with Nonmonotonic Criterion Functions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, Conference B: Computer Vision & Image Processing, vol. 2 (1994)
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Tai, SK., Li, CY., Jan, YJ., Lin, SC. (2010). The Grading of Prostatic Cancer in Biopsy Image Based on Two Stages Approach. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16732-4_26
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DOI: https://doi.org/10.1007/978-3-642-16732-4_26
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