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Proceedings Paper

Region-based graph cut using hierarchical structure with application to ground-glass opacity pulmonary nodules segmentation
Author(s): Chi-Hsuan Tsou; Kuo-Lung Lor; Yeun-Chung Chang; Chung-Ming Chen
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Paper Abstract

Image segmentation for the demarcation of pulmonary nodules in CT images is intrinsically an arduous task. The difficulty can be summarized into two aspects. Firstly, lung tumor can be various in terms of physical densities in pulmonary regions, implying the different interpretation as a mixture of GGO and solid nodules. Hence, processing of lung CT images may generally encounter tissue inhomogeneous problem. The second factor that complicates the task of nodule demarcation is the irregular shapes that most nodules are directly connected to other structures sharing the similar density profile. In this paper, an image segmentation framework is proposed by unifying the techniques of statistical region merging and conditional random field (CRF) with graph cut optimization to address the difficult problem of GGO nodules quantification in CT images. Different from traditional segmentation methods that use pixel-based approach such as region growing and morphological constraints, we employ a hierarchical segmentation tree to alleviate the effect of inhomogeneous attenuation. In addition to building perceptual prominent regions, we perform inference in CRF model based on restricting the pool of segmented regions. Following that, an inference CRF model is carried out to detect and localize individual object instances in CT images. The proposed algorithm is evaluated with four sets of manual delineations on 77 lung CT images. Incorporating with the efficiency and accuracy of pulmonary nodules segmentation method proposed in this paper, a computer aided system is hence feasible to develop related clinical application.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866906 (13 March 2013); doi: 10.1117/12.2006562
Show Author Affiliations
Chi-Hsuan Tsou, National Taiwan Univ. (Taiwan)
Kuo-Lung Lor, National Taiwan Univ. (Taiwan)
Yeun-Chung Chang, National Taiwan Univ. Hospital (Taiwan)
Chung-Ming Chen, National Taiwan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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