Wavelet detection of clustered microcalcifications
Donald A. McCandless, Steven K. Rogers, Jeffrey W. Hoffmeister, Dennis W. Ruck, Richard A. Raines, Bruce W. Suter · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
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%).