Evaluation of information‐theoretic similarity measures for content‐based retrieval and detection of masses in mammograms

The purpose of this study was to evaluate image similarity measures employed in an information‐theoretic computer‐assisted detection (IT‐CAD) scheme. The scheme was developed for content‐based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT‐CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT‐CAD scheme provides an evidence‐based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy‐based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT‐CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in‐house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge‐based decisions regarding the presence of a true mass in the query location. In addition, the IT‐CAD scheme yielded a substantial reduction in false‐positive detections while maintaining high detection rate for malignant masses.