A robust approach for automated lung segmentation in thoracic CT

Zhou, Hailing, Goldgof, Dmitry, Hawkins, Samuel, Wei, Lei, Liu, Ying, Creighton, Doug, Gillies, Robert, Hall, Lawrence O. and Nahavandi, Saeid 2015, A robust approach for automated lung segmentation in thoracic CT, in SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 2267-2272, doi: 10.1109/SMC.2015.396.

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Title A robust approach for automated lung segmentation in thoracic CT
Author(s) Zhou, HailingORCID iD for Zhou, Hailing orcid.org/0000-0001-5009-4330
Goldgof, Dmitry
Hawkins, Samuel
Wei, LeiORCID iD for Wei, Lei orcid.org/0000-0001-8267-0283
Liu, Ying
Creighton, DougORCID iD for Creighton, Doug orcid.org/0000-0002-9217-1231
Gillies, Robert
Hall, Lawrence O.
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name Systems, Man, and Cybernetics. Conference (2015: Hong Kong, China)
Conference location Hong Kong, China
Conference dates 9-12 Oct. 2015
Title of proceedings SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2015
Series IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
Start page 2267
End page 2272
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Computer Science, Cybernetics
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
Lung segmentation
Computed tomography (CT)
computer-aided diagnosis (CAD)
image segmentation
Summary Lung segmentation in thoracic computed tomography (CT) scans is an important preprocessing step for computer-aided diagnosis (CAD) of lung diseases. This paper focuses on the segmentation of the lung field in thoracic CT images. Traditional lung segmentation is based on Gray level thresholding techniques, which often requires setting a threshold and is sensitive to image contrasts. In this paper, we present a fully automated method for robust and accurate lung segmentation, which includes a enhanced thresholding algorithm and a refinement scheme based on a texture-aware active contour model. In our thresholding algorithm, a histogram based image stretch technique is performed in advance to uniformly increase contrasts between areas with low Hounsfield unit (HU) values and areas with high HU in all CT images. This stretch step enables the following threshold-free segmentation, which is the Otsu algorithm with contour analysis. However, as a threshold based segmentation, it has common issues such as holes, noises and inaccurate segmentation boundaries that will cause problems in future CAD for lung disease detection. To solve these problems, a refinement technique is proposed that captures vessel structures and lung boundaries and then smooths variations via texture-aware active contour model. Experiments on 2,342 diagnosis CT images demonstrate the effectiveness of the proposed method. Performance comparison with existing methods shows the advantages of our method.
ISSN 1062-922X
Language eng
DOI 10.1109/SMC.2015.396
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081789

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