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Proceedings Paper

An improved region-growing algorithm for mammographic mass segmentation
Author(s): Ying Cao; Shunren Xia
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Paper Abstract

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.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971O (30 October 2009); doi: 10.1117/12.833044
Show Author Affiliations
Ying Cao, Zhejiang Univ. (China)
Shunren Xia, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)

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