Regrouping initial CAD mass detections to facilitate classification of suspicious regions in mammography

Saskia van Engeland; Nico Karssemeijer

doi:10.1117/12.595205

29 April 2005 Regrouping initial CAD mass detections to facilitate classification of suspicious regions in mammography

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595205
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

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.

Citation Download Citation

Saskia van Engeland and Nico Karssemeijer "Regrouping initial CAD mass detections to facilitate classification of suspicious regions in mammography", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595205

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