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Microcalcification detection based on localized texture comparison

While microcalcifications (MCs) are important early signs of breast cancers, their reliable detection from mammograms has been largely elusive for both radiologists and computer-aided diagnosis (CAD) strategies. Two of the essential components in a CAD system are the detection of the suspicious MC pixels/regions using image processing and analysis techniques, and the training, classification, and recognition of these areas based on pattern recognition methods. In this paper, we present a novel scheme to identify and classify microcalcifications based on localized texture comparison. Relying on a texture removal and repairing (R&R) process of the preselected suspicious areas from their surrounding background tissues, pre- and post- R&R local characteristic features of these areas are extracted and compared. A modified AdaBoost algorithm is then adopted to train the classifier using expert-labelled microcalcifications, followed by a clustering process. Experiments with the mammographic images from the MIAS and DDSM databases have shown very promising results.