Abstract
Mass segmentation in mammograms is a challenging task due to problems such as low contrast, ill-defined, fuzzy or spiculated borders, and the presence of intensity inhomogeneities. These facts complicate the development of computer-aided diagnosis (CAD) systems to assist radiologists. In this paper, a novel mass segmentation algorithm for mammograms based on robust multiscale feature-fusion, and automatic estimation based maximum a posteriori (MAP) method is presented. The proposed segmentation technique consists of mainly four stages: a dynamic contrast improvement scheme applied to a selected region-of-interest (ROI), background-influence correction by template matching, detection of mass candidate points by prior and posterior probabilities based on robust multiscale feature-fusion, and final delineation of the mass region by a MAP scheme. This segmentation method is applied to 480 ROI masses that used ground truth from two radiologists. To compare its effectiveness with the state-of-the-art segmentation methods, three statistical metrics are employed. The experimental results indicate that the developed methods can reliably segment ill-defined or spiculated lesions when compared to other algorithms. Its integration in a CAD system may result in an improved aid to radiologists.
Published Version
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