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Proceedings Paper

Segmentation of kidney using C-V model and anatomy priors
Author(s): Jinghua Lu; Jie Chen; Juan Zhang; Wenjia Yang
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Paper Abstract

This paper presents an approach for kidney segmentation on abdominal CT images as the first step of a virtual reality surgery system. Segmentation for medical images is often challenging because of the objects' complicated anatomical structures, various gray levels, and unclear edges. A coarse to fine approach has been applied in the kidney segmentation using Chan-Vese model (C-V model) and anatomy prior knowledge. In pre-processing stage, the candidate kidney regions are located. Then C-V model formulated by level set method is applied in these smaller ROI, which can reduce the calculation complexity to a certain extent. At last, after some mathematical morphology procedures, the specified kidney structures have been extracted interactively with prior knowledge. The satisfying results on abdominal CT series show that the proposed approach keeps all the advantages of C-V model and overcome its disadvantages.

Paper Details

Date Published: 14 November 2007
PDF: 6 pages
Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 678911 (14 November 2007); doi: 10.1117/12.750016
Show Author Affiliations
Jinghua Lu, Beijing Institute of Technology (China)
Jie Chen, Beijing Institute of Technology (China)
Juan Zhang, Beijing Institute of Technology (China)
Wenjia Yang, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 6789:
MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Jianguo Liu; Kunio Doi; Patrick S. P. Wang; Qiang Li, Editor(s)

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