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Proceedings Paper

Lung segmentation from HRCT using united geometric active contours
Author(s): Junwei Liu; Chuanfu Li; Jin Xiong; Huanqing Feng
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Paper Abstract

Accurate lung segmentation from high resolution CT images is a challenging task due to various detail tracheal structures, missing boundary segments and complex lung anatomy. One popular method is based on gray-level threshold, however its results are usually rough. A united geometric active contours model based on level set is proposed for lung segmentation in this paper. Particularly, this method combines local boundary information and region statistical-based model synchronously: 1) Boundary term ensures the integrality of lung tissue.2) Region term makes the level set function evolve with global characteristic and independent on initial settings. A penalizing energy term is introduced into the model, which forces the level set function evolving without re-initialization. The method is found to be much more efficient in lung segmentation than other methods that are only based on boundary or region. Results are shown by 3D lung surface reconstruction, which indicates that the method will play an important role in the design of computer-aided diagnostic (CAD) system.

Paper Details

Date Published: 14 November 2007
PDF: 7 pages
Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67890M (14 November 2007); doi: 10.1117/12.748487
Show Author Affiliations
Junwei Liu, Univ. of Science and Technology (China)
Chuanfu Li, Univ. of Science and Technology (China)
Anhui Traditional Medicine College (China)
Jin Xiong, Univ. of Science and Technology (China)
Huanqing Feng, Univ. of Science and 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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