Comparative evaluation of pattern recognition techniques for detection of microcalcifications
Kevin S. Woods, Jeffrey L. Solka, Carey E. Priebe, Christopher C. Doss, Kevin W. Bowyer, Laurence P. Clarke · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
Computer detection of microcalcifications in mammographic images will likely require a multi-stage algorithm that includes segmentation of possible microcalcifications, pattern recognition techniques to classify the segmented objects, a method to determine if a cluster of calcifications exists, and possibly a method to determine the probability of malignancy. This paper will focus on the classification of segmented objects as being either (1) microcalcifications or (2) non-microcalcifications. Six classifiers (2 Bayesian, 2 dynamic neural networks, a standard backpropagation network, and a K-nearest neighbor) are compared. Methods of segmentation and feature selection are described, although they are not the primary concern of this paper. A database of digitized film mammograms is used for training and testing. Detection accuracy is compared across the six methods.