Computer-aided detection of breast masses on mammograms: bilateral analysis for false positive reduction

Abstract

In this study, our purpose was to develop a false positive (FP) reduction method for computerized mass detection systems based on the analysis of bilateral mammograms. We first detect the mass candidates on each view by utilizing our unilateral computer-aided detection (CAD) system. For each detected object, the regional registration technique is used to define a region of interest (ROI) that is symmetrical to the object location on the contralateral mammogram. Spatial gray level dependence matrices (SGLD) texture features and morphological features are extracted from both the ROI containing the detected object on a mammogram and its corresponding ROI on the contralateral mammogram. Bilateral features are then generated from the extracted unilateral features and a final bilateral score is formed as a new feature to differentiate symmetric from asymmetric ROIs. By incorporating the unilateral features of the mass candidates and their bilateral scores, a bilateral classifier was trained to reduce the FPs. It was found that our bilateral CAD system achieved a case-based sensitivity of 70%, 80%, and 85% at 0.52, 0.83, and 1.05 FPs/image on the test data set. In comparison to the FP rates for the unilateral CAD system of 0.67, 1.11, and 1.69, respectively, at the corresponding sensitivities, the FP rates were reduced by 22%, 25%, and 37% with the bilateral symmetry information.