Accurate Microcalcification (MC) segmentation is acrucial first step in morphology based Computer Aided Diagnosissystems for microcalcifications in mammography. In this articlewe present an automated segmentation method of individualMCs adaptive to both size and shape variations. Size is estimatedby active rays (polar-transformed active contours) on continuouswavelet representation while shape adaptivity is achieved by asubsequent region growing step. Following MC seed pointannotation, contour point estimates are obtained byimplementing active rays on an analytic scale-spacerepresentation in a coarse-to-fine strategy. Initial coarsest scale iautomatically defined by analyzing MC responses across scales.A region growing method is used to delineate the final MCcontour curve, with pixel aggregation constrained by the MCcontour point estimates. The segmentation accuracy of theproposed method was quantitatively evaluated by means of areaoverlap by comparing automatically derived borders withmanually traced ones provided by an expert radiologist. The roposed method achieved an area overlap of 0.68±0.13 on adataset of 67 individual microcalcifications, originating frompleomorphic clusters.