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...

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Bibliographic Details
Published in:IEEE transactions on information technology in biomedicine 1997-06, Vol.1 (2), p.141-149
Main Authors: Bankman, I.N., Nizialek, T., Simon, I., Gatewood, O.B., Weinberg, I.N., Brody, W.R.
Format: Article
Language:English
Subjects:
Active contours
Algorithms
Breast cancer
Breast Neoplasms - diagnostic imaging
Calcification (biochemistry)
Calcinosis - diagnostic imaging
Cancer detection
Clustering algorithms
Computer Simulation
Data models
Data structures
Diagnosis, Computer-Assisted
Diseases
Edge detection
Female
Humans
Image edge detection
Image segmentation
Mammography - statistics & numerical data
Parametric statistics
Phase detection
Radiology
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Summary: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, which operates without the aspects mentioned above, and the active contour model, which 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.
ISSN:1089-7771
1558-0032
DOI:10.1109/4233.640656