Using context for mass detection and classification in mammograms

In mammography, computer-aided diagnosis (CAD) techniques for mass detection and classification mainly use local image information to determine whether a region is abnormal or not. There is a lot of interest in developing CAD methods that use context, asymmetry, and multiple view information. However, it is not clear to what extent this may improve CAD results. In this study, we made use of human observers to investigate the potential benefit of using context information for CAD. We investigated to what extent human readers make use of context information derived from the whole breast area and from asymmetry for the tasks of mass detection and classification. Results showed that context information can be used to improve CAD programs for mass detection. However, there is still a lot to be gained from improvement of local feature extraction and classification. This is demonstrated by the fact that the observers did much better in classifying true positive (TP) and false positive (FP) regions than the CAD program. For classification of benign and malignant masses context seems to be less important.