Content-based image retrieval as a computer aid for the detection of mammographic masses

The purpose of the study was to develop and evaluate a content-based image retrieval (CBIR) approach as a computer aid for the detection of masses in screening mammograms. The study was based on the Digital Database for Screening Mammography (DDSM). Initially, a knowledge database of 1,009 mammographic regions was created. They were all 512×512 pixel ROIs with known pathology. Specifically, there were 340 ROIs depicting a biopsy-proven malignant mass, 341 ROIs with a benign mass, and the remaining 328 ROIs were normal. Subsequently, the CBIR algorithm was implemented using mutual information (MI) as the similarity metric for image retrieval. The CBIR algorithm formed the basis of a knowledge-based CAD system. The system operated as follows. Given a databank of mammographic regions with known pathology, a query suspicious mammographic region was evaluated. Based on their information content, all similar cases in the databank were retrieved. The matches were rank-ordered and a decision index was calculated using the query's best matches. Based on a leave-one out sampling scheme, the CBIR-CAD system achieved an ROC area index A_Z= 0.87±0.01 and a partial ROC area index _0.90A_Z = 0.45±0.03 for the detection of masses in screening mammograms.