Connective tissue representation for detection of microcalcifications in digital mammograms

Progress is reported towards an automated computer aided detection system for clustered microcalcifications utilizing two image feature parameters: local contrast and shape, which offers an improvement over segmentation results obtained without the shape parameter.

Abstract

Microcalcification clusters appear as an early sign of breast cancer and play an important role in interpreting mammograms. Progress is reported towards an automated computer aided detection system for clustered microcalcifications utilizing two image feature parameters: local contrast and shape. The use of a shape parameter is necessary to distinguish thin patches of connective tissue from microcalcifications. Two shape parameter techniques are compared in the segmentation of 15 digital mammogram images. The first technique implements the linear Hough transform, while the second uses image phase information in the Fourier domain. In both cases labeling of the image is performed by a deterministic relaxation scheme, in which both image data dn prior beliefs are weighted simultaneously. Similar segmentation results are obtained for each shape parameter technique however the execution time for the phase method is approximately one quarter that for the Hough method. Both techniques offer an improvement over segmentation results obtained without the shape parameter.