The use of computer research for breast cancer diagnosis in digital mammograms has been studied by some researchers for years. The researches based on medical image processing were developed and published continuously. Theirs objective are to create a diagnostic tool that can increase the accuracy of risk analysis for breast cancer. At the early stage, cancers may be identified as spiculated masses revealing architectural distortion. This research proposes a semi-automated method to detect architectural distortion characterized by thin lines radiating from its margins. It will help physicians as second or minor opinion before biopsy operation. The proposed method involves following major steps in sequence. A combination of the object attributes thresholding, hill-climbing and region growing algorithm is applied to digital mammogram for background and breast pectoral muscle removal. The second is a region of interest (ROI) selection based on image splitting and breast ratio estimation. In the third step, the shade corrections of ROI are considered by using the contrast-limited adaptive histogram equalization. Next, we apply the modified hierarchical clustering to detect and enhance the possible cluster of spiculated masses. The other clusters will be a significant reduction. The final step is established to segment spiculated shape by employing the parametric active contour method. The numerical experiments of the proposed method are performed by testing on the digital database for screening mammography (DDSM) made up by the University of South Florida.