Segmentation algorithms for detecting microcalcifications in mammograms.
The presence of microcalcification clusters in mammograms contributes evidence for the diagnosis of early stages of breast cancer. In many cases, microcalcifications are subtle and their detection can benefit from an automated system serving as a diagnostic aid. The potential contribution of such a system may become more significant as the number of mammograms screened increases to levels that challenge the capacity of radiology clinics. Many techniques for detecting microcalcifications start with a segmentation algorithm that indicates all candidate structures for the subsequent phases. Most algorithms used to segment microcalcifications have aspects that might raise operational difficulties, such as thresholds or windows that must be selected, or parametric models of the data. We present a new segmentation algorithm and compare it to two other algorithms: the multi-tolerance region growing algorithm that operates without the aspects mentioned above, and the active contour model that has not been applied previously to segment microcalcifications. The new algorithm operates without threshold or window selection, or parametric data models, and it is more than an order of magnitude faster than the other two.
Bankman
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Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723, USA. isaac.bankman@jhuapl.edu
Nizialek
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Simon
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Gatewood
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Weinberg
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IN
Brody
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D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*