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A novel clustering method is proposed for mammographic mass segmentation on extracted regions of interest (ROIs) by using deterministic annealing incorporating circular shape function (DACF). The objective function reported in thisstudy uses both intensity and spatial shape information, and the dominant dissimilarity measure is controlled by two weightingparameters. As a result, pixels having similar intensity information but located in different regions can bedifferentiated. Experimental results shows that, by using DACF, the mass segmentation results in digitized mammograms are improvedwith optimal mass boundaries, less number of noisy patches, and computational efficiency. An average probability of segmentationerror of 7.18% for well-defined masses (or 8.06% for ill-defined masses) was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55%) and 6.14% (or 5.27%) improvements as compared to the standard DA and fuzzy c-means methods.

A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation