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Vol: 58(72) No: 1 / March 2013 Simple Texture Descriptors for Classification of Mitotic Cells Figures of Histopathological Images from Breast Cancer Mircea-Sebastian Serbanescu Department of Medical Informatics, University of Medicine and Pharmacy of Craiova, Faculty of Medicine, Petru Rares St. , No. 2, 200349, Craiova, Romania, phone: (0040) 351-443-561, e-mail: mircea_serbanescu@yahoo.com Keywords: mitosis detection, computer aided diagnosis, automated mitotic index Abstract Confirmation of clinical breast cancer diagnosis is done histopathologically on microscopic slides taking into consideration cell modifications, architecture modifications and mitotic cell index. Mitotic cells count (multiplying cells) is a separate index represented by counting the number of multiplying cells figures in at least 10 high power magnification microscopic fields (20x, 40x). It is a time consuming and demanding method, with low reproducibility, so it is suitable for an automated (computer aided) method. A total number of 9099 cell nucleus images, with 184 mitotic figures, were obtained from 10 high power microscope fields (40x, 0.2456 µm/pixel) of breast cancer images. All mitotic figures were manually annotated by several pathologists. Our study aimed to see if simple texture descriptors (Contrast, Correlation, Energy, Homogeneity, Entropy) are suitable for an automated diagnosis of mitotic cells. We applied a k-means clustering algorithm to see the aggregation of mitotic and non-mitotic cells image descriptors. No clear cluster of mitotic figures was obtained. There were some non-mitotic, smaller, compact clusters but we found them irrelevant for our study. Furthermore we have trained a feed foreword neural network with two hidden layers for the classification task. The overall prediction of the network was poor. 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