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Proceedings Paper

Automatic detection and classification system for calcifications in mammograms
Author(s): Liang Shen; Rangaraj M. Rangayyan; J. E. Leo Desautels
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Paper Abstract

In this paper, we propose an automatic calcification detection and classification system. First, a new multi-tolerance region growing method is proposed for the detection of potential calcification regions and extraction of their contours. The method employs a distance metric computed on feature sets including measures of shape, center of gravity, and size obtained for various growth tolerance values in order to determine the most suitable parameters. Then, shape features from moments, Fourier descriptors, and compactness are computed based upon the contours of the regions. Finally, a two-layer perceptron is utilized for the purpose of calcification classification with the shape features. In our preliminary study, detection rates were 87% and 85%, and correct classification rates were 94% and 87% for 54 benign calcifications and 241 malignant calcifications, respectively. The proposed system should provide considerable help to radiologists in the diagnosis of breast cancer.

Paper Details

Date Published: 29 July 1993
PDF: 7 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148691
Show Author Affiliations
Liang Shen, Univ. of Calgary (Canada)
Rangaraj M. Rangayyan, Univ. of Calgary (Canada)
J. E. Leo Desautels, Foothills Hospital (Canada)

Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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