Mammographic mass detection using unsupervised clustering in synergy with a parcimonious supervised rule-based classifier

Abstract

We develop a novel CAD detection system that can help a radiologist to detect masses in mammograms. The proposed algorithm concurrently detects the breast boundary and the pectoral muscle. Then, a clustering and morphology based segmentation algorithm is applied to the enhanced mammography image to separate the mass from the normal breast tissues. This technique outlines the shape of candidate masses in mammograms. To maximize detection specificity, we develop a two-stage hybrid classification network. First, an unsupervised classifier is used to classify suspicious opacities as suspect or not. Then, a few supervised interpretation rules are applied to further reduce the number of false detections. Using a private mammography database and the publicly available USF/DDSM database, experimental results demonstrate that a sensitivity of 94% (resp. 80%) can be achieved at a specificity level of 1.02 (resp. 0.69) false positives per image. Even in dense mammograms, the CAD algorithm can still correctly detect subtle masses. © Springer-Verlag Berlin Heidelberg 2006.