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Journal of Electronic Imaging

Confidence shape metric for image segmentation
Author(s): Qi Zou; Siwei Luo; Jingjing Zhong; Liping Yang
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Paper Abstract

Here, we propose a confidence shape metric for level set segmentation. First, the confidence shape metric, which encodes local confidence into the matching result, is used in matching shapes and producing confidence maps. Then, based on the confidence shape prior, the level set function evolves to a desired contour. The proposed shape metric allows representation of shape variations beyond the coverage of training shapes under the level set framework, which is suitable for segmenting strongly deformed and cluttered images, especially when the set of training shapes is sparse compared with numerous intracategory variations. We evaluated the proposed approach on the challenging Weizmann dataset and computed tomography images. Experimental results indicate the advantage of confidence shape prior over shape prior without confidence under the Dice-coefficient metric.

Paper Details

Date Published: 10 May 2013
PDF: 13 pages
J. Electron. Imaging. 22(2) 023009 doi: 10.1117/1.JEI.22.2.023009
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Qi Zou, Beijing Jiaotong Univ. (China)
Siwei Luo, Beijing Jiaotong Univ. (China)
Jingjing Zhong, National Library of China (China)
Liping Yang, Beijing Jiaotong Univ. (China)

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