Microcalcification Detection using a Kernel Bayes Classifier

Mammography associated with clinical breast examination is the only effective method for mass breast screening. Microcalcifications are one of the primary signs for early detection of breast cancer. In this paper we propose a new kernel method for classification of dificult to diagnose regions in mammographic images. It consists of a novel class of Markov Random Fields, using techniques developed within the context of statistical mechanics., This method is used for the classification of positive Region of Interest (ROI's) containing clustered microcalcifications and negative ROI's containing normal tissue. We benchmarked the new proposed method with a nearest neighbor classifier and with an artificial neural network, widely used in literature for computer-aided diagnosis. We obtained the best performance using the novel approach.