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Abstract. This paper presents and tests a methodology that sinergically combines a select of successful advances in each step to automatically classify microcalcifications (MCs) in digitized mammography. The method combines selection of regions of interest (ROI), enhancement by histogram adaptive techniques, processing by multiscale wavelet and gray level statistical techniques, generation, clustering and labelling of suboptimal feature vectors (SFVs), and a Neural feature selector and detector to finally classify the MCs. The experimental results with the method promise interesting advances in the problem of automatic detection and classification of MCs 1 .

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A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection.