Size-adapted segmentation of individual mammographic microcalcifications

Accurate Microcalcification (MC) segmentation is a crucial first step in morphology based computer aided diagnosis systems for microcalcifications in mammography. In this article we present an automated segmentation method of individual MCs adaptive to both size and shape variations. Size is estimated by active rays (polar-transformed active contours) on continuous wavelet representation while shape adaptivity is achieved by a subsequent region growing step. Following MC seed point annotation, contour point estimates are obtained by implementing active rays on an analytic scale-space representation in a coarse-to-fine strategy. Initial coarsest scale is automatically defined by analyzing MC responses across scales. A region growing method is used to delineate the final MC contour curve, with pixel aggregation constrained by the MC contour point estimates. The segmentation accuracy of the proposed method was quantitatively evaluated by means of area overlap by comparing automatically derived borders with manually traced ones provided by an expert radiologist. The proposed method achieved an area overlap of 0.68plusmn0.13 on a dataset of 67 individual microcalcifications, originating from pleomorphic clusters.