An improved method of region grouping for microcalcification detection in digital mammograms

A very important issue, namely region grouping, in computer-assisted diagnostic (CAD) detection of microcalcification clusters in digital mammograms is addressed in this work. In the diagnosis of breast cancer, microcalcification cluster, instead of single and isolated microcalcifications, are considered clinically significant. Grouping individual regions segmented from digital mammograms, therefore, should be a component in an automatic microcalcification cluster detection system. Actually this component may concern several system modules, such as segmentation, feature extraction, performance estimation aiming at both algorithm optimization and consistent evaluation and ultimately computerized malignancy estimation of calcified lesions. The previous work in the literature used a kernel-based method for region grouping. We propose a distance-based and dense-to-sparse grouping method. The grouping result should be independent of the size, shape and orientation of real clusters.