A very important issue, namely region grouping, in computer-assisted diagnostic detection of microcalcification clusters (MCC) in digital mammograms is addressed in this work. In the diagnosis of breast cancer, MCC, 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 MCC 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 proposed a distance-based and dense-to-sparse grouping method. The grouping result should be independent of the size, shape and orientation of real clusters. The application, namely cluster-oriented analysis including an adaptive segmentation method and cluster level feature extraction scheme, is discussed. A preliminary study was performed on a set of 30 full mammograms at 60 microm resolution, containing 40 MCC. The introduction of the cluster level feature extraction and a simple rule-based method reduces false positives from 7.1 to 2.4 per image at the sensitivity of 92.5%. This grouping method provides a solid basis for effective feature extraction-analysis and candidate cluster classification.