Mammographic mass segmentation based on maximum entropy principle and active contour model

Mammographic mass segmentation plays a crucial role in computer-aided scheme (CAD). In this paper, we propose a method based on maximum entropy principle and active contour model to do segmentation. There are two main steps in this method. First, maximum entropy principle was applied on the background-trend corrected regions of interest (ROIs) to obtain the initially detected outlines. Secondly, active contour model was used to refine the initially detected outlines of the masses. The regions of interest used in this study were extracted from images in the Digital Database for Screening Mammography (DDSM) provided by the University of South Florida. The preliminary experimental results are encouraging. The segmentation algorithm performs robustly and well for various types of masses. The overlap criterion analysis shows that the proposed segmentation results are more similar to radiologists' manual segmentation compared with other experimented methods.

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