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.
[1]
N Karssemeijer,et al.
Automated classification of parenchymal patterns in mammograms.
,
1998,
Physics in medicine and biology.
[2]
Lubomir M. Hadjiiski,et al.
Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.
,
2001,
Medical physics.
[3]
Nico Karssemeijer,et al.
Detection of stellate distortions in mammograms
,
1996,
IEEE Trans. Medical Imaging.
[4]
Nico Karssemeijer,et al.
Using Information from Two Mammographic Views to Improve Computer-Aided Detection of Mass Lesions
,
2003
.
[5]
N. Karssemeijer,et al.
A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.
,
2004,
Medical physics.
[6]
Nico Karssemeijer,et al.
Single and multiscale detection of masses in digital mammograms
,
1999,
IEEE Transactions on Medical Imaging.
[7]
Berkman Sahiner,et al.
Improvement of computerized mass detection on mammograms: fusion of two-view information.
,
2002,
Medical physics.