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Accurate airway centerline extraction based on topological thinning using graph-theoretic analysis

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Bian, Zijian; | Tan, Wenjun; ; | Yang, Jinzhu; | Liu, Jiren | Zhao, Dazhe;

Affiliations: Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 3-11, Wenhua Road, Heping District, Shenyang, P.R. China | Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, No. 3-11, Wenhua Road, Heping District, Shenyang, P.R. China | College of Information Science and Engineering, Northeastern University, No. 3-11, Wenhua Road, Heping District, Shenyang, P.R. China

Note: [] Corresponding author: Wenjun Tan, Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, No. 3-11, Wenhua Road, Heping District, Shenyang, P.R. China. Tel.: +86 130 792 23202; Fax: +86 024 836 63446; E-mail: [email protected].

Keywords: Airway tree, centerline extraction, topological thinning, graph-theoretic analysis

DOI: 10.3233/BME-141146

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3239-3249, 2014

Published: 2014
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Abstract

The quantitative analysis of the airway tree is of critical importance in the CT-based diagnosis and treatment of popular pulmonary diseases. The extraction of airway centerline is a precursor to identify airway hierarchical structure, measure geometrical parameters, and guide visualized detection. Traditional methods suffer from extra branches and circles due to incomplete segmentation results, which induce false analysis in applications. This paper proposed an automatic and robust centerline extraction method for airway tree. First, the centerline is located based on the topological thinning method; border voxels are deleted symmetrically to preserve topological and geometrical properties iteratively. Second, the structural information is generated using graph-theoretic analysis. Then inaccurate circles are removed with a distance weighting strategy, and extra branches are pruned according to clinical anatomic knowledge. The centerline region without false appendices is eventually determined after the described phases. Experimental results show that the proposed method identifies more than 96% branches and keep consistency across different cases and achieves superior circle-free structure and centrality.

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