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|Title:||An unsupervised scheme for detection of microcalcifications on mammograms|
|Citation:||Proceedings of the International Conference on Image Processing (V 1), Vancouver, Canada, 10-13 September 2000, 184-187|
|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.|
|Appears in Collections:||Proceedings papers|
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