14 November 2007 Mammographic mass segmentation based on maximum entropy principle and active contour model
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Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 67891G (2007) https://doi.org/10.1117/12.751073
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
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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Enmin Song, Enmin Song, Luan Jiang, Luan Jiang, Jinhui Liu, Jinhui Liu, Renchao Jin, Renchao Jin, Xiangyang Xu, Xiangyang Xu, } "Mammographic mass segmentation based on maximum entropy principle and active contour model", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67891G (14 November 2007); doi: 10.1117/12.751073; https://doi.org/10.1117/12.751073
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