Computer-assisted diagnosis of mammographic masses using an information-theoretic image retrieval scheme with BIRADs-based relevance feedback

The purpose of the study was to develop and evaluate a content-based image retrieval (CBIR) approach for computer-assisted diagnosis of masses detected in screening mammograms. The system follows an information theoretic retrieval scheme with a BIRADS-based relevance feedback (RF) algorithm. Initially, a knowledge databank of 365 mammographic regions of interest (ROIs) was created. They were all 512×512 pixel ROIs extracted from DDSM mammograms digitized using the Lumisys digitizer. The ROIs were extracted around the known locations of the annotated masses. Specifically, there were 177 ROIs depicting a biopsy-proven malignant mass and 188 ROIs with a benign mass. 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. Given a databank of mammographic masses with known pathology, a query mass was evaluated. Based on their information content, all similar masses in the databank were retrieved. A relevance feedback algorithm based on BIRADS findings was implemented to determine the relevance factor of the retrieved masses. Finally, a decision index was calculated using the query's k best matches. The decision index effectively combined the similarity metric of the retrieved cases and their relevance factor into a prediction regarding the malignancy status of the mass depicted in the query ROI. ROC analysis was to evaluate diagnostic performance. Performance improved dramatically with the incorporation of the relevance feedback algorithm. Overall, the CAD system achieved ROC area index A _Z= 0.86±0.02 for the diagnosis of masses in screening mammograms.