Computer Graphics and Visual Computing (CGVC) 2018

Breast cancer continues to be the most common type of cancer among women. Early detection of breast cancer is key to effective treatment. The presence of clusters of fine, granular microcalcifications in mammographic images can be a primary sign of breast cancer. The malignancy of any cluster of microcalcification cannot be reliably determined by radiologists from mammographic images and need to be assessed through histology images. In this paper, a novel method of mammographic microcalcification classification is described using the local topological structure of microcalcifications. Unlike the statistical and texture features of microcalcifications, the proposed method focuses on the number of microcalcifications in local clusters, the distance between them, and the number of clusters. The initial evaluation on the Digital Database for Screening Mammography (DDSM) database shows promising results with 86% accuracy and findings which are in line with clinical perception of benign and malignant morphological appearance of microcalcification clusters.

Keywords: microcalcification classification, benign/malignant, topological modelling, graph connected chain

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