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Proceedings Paper

Wavelet detection of clustered microcalcifications
Author(s): Donald A. McCandless; Steven K. Rogers; Jeffrey W. Hoffmeister M.D.; Dennis W. Ruck; Richard A. Raines; Bruce W. Suter
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Paper Abstract

An automated method for detecting microcalcification clusters is presented. The algorithm begins with a digitized mammogram and outputs the center coordinates of regions of interest (ROIs). The method presented uses a non-linear function and a 12-tap least asymmetric Daubechies (LAD12) wavelet in a tree structured filter bank to increase the signal to noise level by 10.26 dB. The signal to noise level gain achieved by the filtering allows subsequent thresholding to eliminate on average 90% of the image from further consideration without eliminating actual microcalcification clusters 95% of the time. Morphological filtering and texture analysis are then used to identify individual microcalcifications. Altogether, the method successfully detected 44 of 53 microcalcification clusters (83%) with an average of 2.3 false positive clusters per image. A cluster is considered detected if it contains 3 or more microcalcifications within a 6.4 mm by 6.4 mm area. The method successfully detected 13 of the 14 malignant cases (93%).

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); doi: 10.1117/12.236012
Show Author Affiliations
Donald A. McCandless, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Jeffrey W. Hoffmeister M.D., Air Force Institute of Technology (United States)
Dennis W. Ruck, Air Force Institute of Technology (United States)
Richard A. Raines, Air Force Institute of Technology (United States)
Bruce W. Suter, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2762:
Wavelet Applications III
Harold H. Szu, Editor(s)

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