Automatic segmentation of microcalcification clusters

Abstract

Early detection of microcalcification (MC) clusters plays a crucial role in enhancing breast cancer diagnosis. Two automated MC cluster segmentation techniques are proposed based on morphological operations that incorporate image decomposition and interpolation methods. For both approaches, initially the contrast between the background tissue and MC cluster was increased and subsequently morphological operations were used. Evaluation was based on the Dice similarity scores and the results of MC cluster classification. A total number of 248 (131 benign and 117 malignant) and 24 (12 benign and 12 malignant) biopsy-proven digitized mammograms were considered from the DDSM and MIAS databases, which showed a classification accuracy of 94.48 ± 1.11 % and 100.00 ± 0.00 % respectively.