A. Chekkoury, P. Khurd, Jie Ni, C. Bahlmann, , Amar Patel, L. Grady, M. Singh, M. Groher, N. Navab, , Jeffrey Johnson, Anna Graham, Ronald Weinstein Automated Malignancy Detection in Breast Histopathological Images SPIE Medical Imaging, 04-09 February 2012, San Diego, California, USA (bib)
	Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy samples. Our system uses novel textural, topological and morphometric features taking advantage of the special patterns of the nuclei cells in breast cancer histopathological images. We use a Support Vector Machine classifier on these features to diagnose malignancy. In conjunction with the maximum relevance - minimum redundancy feature selection technique, we obtain high sensitivity and specificity. We have also investigated the effect of image compression on classification performance.
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