Graph-based region growing for mass segmentation in digital mammography

Mass segmentation is a vital step in CAD mass detection and classification. A challenge for mass segmentation in mammograms is that masses may contact with some surrounding tissues, which hate the similar intensity. In this paper, a novel graph-based algorithm has been proposed to segment masses in mammograms. In the proposed algorithm, the procedure of region growing is represented as a growing tree whose root is the selected seed. Active leaves, which have the ability to grow, in the connection area between adjacent regions are deleted to stop growing, then separating the adjacent regions while keeping the spiculation of masses, which is a primary sign of malignancy for masses. The new constrained segmentation was tested with 20 cases in USF moffitt mammography database against the conventional region growing algorithm. The segmented mass regions were evaluated in terms of the overlap area with annotations made by the radiologist. We found that the new graph-based segmentation more closely match radiologists' outlines of these masses.