Title :
Segmentation algorithms for detecting microcalcifications in mammograms
Author :
Bankman, Isaac N. ; Nizialek, Tanya ; Simon, Inpakala ; Gatewood, Olga B. ; Weinberg, Irving N. ; Brody, William R.
Author_Institution :
Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA
fDate :
6/1/1997 12:00:00 AM
Abstract :
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.
Keywords :
diagnostic radiography; edge detection; image segmentation; medical image processing; active contour model; automated system; breast cancer diagnosis; candidate structures; edge detection; image segmentation algorithms; mammograms; microcalcification cluster detection; multi-tolerance region-growing algorithm; parametric data models; radiology clinics; threshold selection; window selection; Active contours; Breast cancer; Cancer detection; Clustering algorithms; Data models; Diseases; Image edge detection; Parametric statistics; Phase detection; Radiology; Algorithms; Breast Neoplasms; Calcinosis; Computer Simulation; Diagnosis, Computer-Assisted; Female; Humans; Mammography;
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
DOI :
10.1109/4233.640656