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A novel lung nodules detection scheme based on vessel segmentation on CT images

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: Jia, Tong; ; | Zhang, Hao | Meng, Haixiu

Affiliations: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China | Key Laboratory of Medical Image Computing, Ministry of Education Shenyang 110819, China

Note: [] Corresponding author: Jia Tong, College of Information Science and Engineering, Northeastern University, Shenyang 110819, China. Tel.: +86 2483688326; Fax: +86 2423891272; E-mail: [email protected].

Keywords: Lung vessel segmentation, lung nodule detection, active contour model, CT image

DOI: 10.3233/BME-141139

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3179-3186, 2014

Published: 2014
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Abstract

Lung vessels often interfere with the detection of lung nodules. In this paper, a novel computer-aided lung nodule detection scheme on vessel segmentation is proposed. This paper describes an active contour model which can combine image region mean gray value and image edge energy. It is used to segment and remove lung vessels. A selective shape filter based on Hessian Matrix is used to detect suspicious nodules and remove omitted lung vessels. This paper extracts density, shape and position features of suspicious nodules, and uses a Rule-Based Classification (RBC) method to identify true positive nodules. In the experiment results, the detection sensitivity is about 90% and FP is 1/scan.

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