An unsupervised scheme for detection of microcalcifications on mammograms

Abstract

Clusters of microcalcifications which appear like small white grains of sand on mammograms are the earliest signs of breast cancer. In this work we employ a Gabor filter bank for texture analysis of mammograms to detect microcalcifications. A subset of the Gabor filter bank with a certain central frequency and different orientations is used to obtain the Gabor-filtered images. The filtered images are then subjected to a histogram based threshold to obtain binary images. Feature vectors are computed using the binary images. A k-means clustering algorithm with a variance scaled Euclidean distance is used for segmentation of the image.