An improved region-growing algorithm for mammographic mass segmentation
Ying Cao, Xin Hao, Shunren Xia · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Segmentation of mammographic masses is a challenging task since masses on mammograms typically have fuzzy and irregular edges. In the case of tissue adhesion, the region growing algorithm combined with maximum likelihood analysis will lead to a problem of over-segmentation. For the reason given above, an improved adaptive region growing algorithm for mass segmentation is proposed in this paper. In this algorithm, a hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis is developed. In order to accommodate different situations of masses, the likelihood and the edge gradients of segmented masses are weighted adaptively by the use of information entropy. 40 benign and 37 malignant tumors were tested in this study. Compared with conventional region growing algorithm, our proposed algorithm is more adaptive and robust, and it could obtain segmentation contour more accurately.