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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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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