Microcalcification Detection Based on Localized Texture Comparison (WA-P8)

Author(s) : Xin Yuan (Hong Kong University of Science and Technology, Hong Kong) Pengcheng Shi (Hong Kong University of Science and Technology, Hong Kong)

Abstract : 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.

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