An unsupervised scheme for detection of microcalcifications on mammograms
Tejashree Bhangale, Urmi Desai, Uzzal Sharma · 2002
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.