Computer-aided detection of mammographic masses based on content-based image retrieval

A method for computer-aided detection (CAD) of mammographic masses is proposed and a prototype CAD system is presented. The method is based on content-based image retrieval (CBIR). A mammogram database containing 2000 mammographic regions is built in our prototype CBIR-CAD system. Every region of interested (ROI) in the database has known pathology. Specifically, there are 583 ROIs depicting biopsy-proven masses, and the rest 1417 ROIs are normal. Whenever a suspicious ROI is detected in a mammogram by a radiologist, it can be submitted as a query to this CBIR-CAD system. As the query results, a series of similar ROI images together with their known pathology knowledge will be retrieved from the database and displayed in the screen in descending order of their similarities to the query R01 to help the radiologist to make the diagnosis decision. Furthermore, our CBIR-CAD system will output a decision index (DI) to quantitatively indicate the probability that the query ROI contains a mass. The DI is calculated by the query matches. In the querying process, 24 features are extracted from each ROI to form a 24-dimensional vector. Euclidean distance in the 24-dimensional feature vector space is applied to measure the similarities between ROIs. The prototype CBIR-CAD system is evaluated based on the leave-one-out sampling scheme. The experiment results showed that the system can achieve a receiver operating characteristic (ROC) area index A(Z) =0.84 for detection of mammographic masses, which is better than the best results achieved by the other known mass CAD systems.