Lung segmentation from HRCT using united geometric active contours

Junwei Liu; Chuanfu Li; Jin Xiong; Huanqing Feng

doi:10.1117/12.748487

14 November 2007 Lung segmentation from HRCT using united geometric active contours

Junwei Liu, Chuanfu Li, Jin Xiong, Huanqing Feng

Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 67890M (2007) https://doi.org/10.1117/12.748487
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Junwei Liu, Chuanfu Li, Jin Xiong, and Huanqing Feng "Lung segmentation from HRCT using united geometric active contours", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67890M (14 November 2007); https://doi.org/10.1117/12.748487

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