There is a lot of interest in developing computer-aided detection (CAD) techniques for mammography that use multiple view information. During the development of such techniques we have noticed that they are hampered by the phenomena that mass lesions are sometimes detected by multiple regions. This has encouraged us to develop a technique to regroup initial CAD detections to facilitate the final classification of suspicious regions. The regrouping technique searches for detections that belong to the same structure. Therefore, it takes into account the distance between the detections and the image structure along a path between the detections. When correspondence is found, the two detections are replaced by a new detection in between the initial detections. Our regrouping technique correctly regrouped the detections in 48 percent of the masses initially detected by multiple regions. Of the false positive detections two percent were combined, and the percentage of true positive - false positive combinations was one. Incorporation of the
algorithm into our CAD scheme resulted in a slight increase in detection performance. In addition, in our multiple view scheme it also resulted in a decrease in the number of incorrectly linked regions in corresponding mammographic views.
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.
In this study we investigate two ways of presenting prior and current mammograms on a mammography workstation: next to each other and alternating at the same display (toggle). The experiment consisted of 420 trials with prior-current mammogram pairs, displayed on a dedicated mammography workstation. In two-alternative forced-choice (2AFC) experiment, observers were asked to select the image containing the largest lesion. The stimuli were created by pasting extracted lesions into normal mammograms. Results showed that the observers preformed more accurate in selecting the largest lesion when using the toggle option.