Automated detection of mammographic masses: Preliminary assessment of an information-theoretic CAD scheme for reduction of false positives

The purpose of this work was to evaluate an information-theoretic computer-aided detection (CAD) scheme for improving the specificity of mass detection in screening mammograms. The study was based on images from the Lumisys set of the Digital Database for Screening Mammography (DDSM). Initially, the craniocaudal views of 49 DDSM mammograms were analyzed using an automated detection algorithm developed to prescreen mammograms. The prescreening algorithm followed a morphological concentric layer analysis and resulted in 319 false positive detections at 92% sensitivity. These 319 suspicious yet normal regions were extracted for further analysis with our information-theoretic CAD scheme. Our scheme follows a knowledge-based decision strategy. The strategy relies on information theoretic principles for similarity assessment between a query case and a knowledge databank of cases with known ground truth. Receiver Operating Characteristic (ROC) analysis was performed to determine how well the CAD scheme can discriminate the false positive regions from 681 true masses. The overall ROC area index of the information-theoretic CAD system was 0.75±0.02. At 97%, 95%, and 90% sensitivity, the system eliminated safely 20%, 30%, and 42% of the previously identified false positives respectively. Thus, information-theoretic CAD analysis can yield a significant reduction in false-positive detections while maintaining reasonable sensitivity.