Detection of Masses from Mammograms Using Mass shape Pattern

The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using mass shape pattern. Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds. Then, a mass shape pattern was used to categorize the ROI as true masses or non-masses based on their morphologies. Each pixel of a ROI was scanned with a mass shape pattern to determine whether there was a shape (part of a ROI) similar to the mass in the shape pattern. The similarity was controlled using two thresholds. If a shape was detected, then the coordinates of the shape were recorded as part of a true mass. To test the system’s efficiency, we applied this process to 52 mammogram images from the Mammographic Image Analysis Society (MIAS) database. Three hundred and thirty-two ROI were identified using the ROI specification methods. These ROI were classified using three mass shape pattern whose diameters were 10, 20 and 30 pixels. The results of this experiment showed that using the mass shape pattern with these diameters achieved sensitivities of 93%, 90% and 81% with 1.3, 0.7 and 0.33 false positives per image respectively. These results indicate that the detection performance of this shape pattern based algorithm is satisfactory, and may improve the performance of computer-aided analysis of mammographic images and early diagnosis of mammographic masses.