Multi-feature prostate cancer diagnosis of histological images using advanced image segmentation Online publication date: Mon, 01-Nov-2010
by S. Subha Rani, A. Kannammal, M.S. Nirmal, K. Vignesh Prabhu, R. Vinoth Kumar
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 2, No. 4, 2010
Abstract: We present a study of image features for cancer diagnosis of the histological images of prostate. In diagnosis, the tissue image is classified into the tumour and non-tumour classes. In Gleason grading, which characterises tumor aggressiveness, the image is classified as containing a low- or high-grade tumour. The primary contribution of this paper is to aggregate colour and texture properties at histological object levels for classification. Features representing different visual cues were combined in a supervised learning framework. We also compare the performance of Gaussian, k-nearest neighbour, and Bayesian classifier.
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