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A robust approach for automated lung segmentation in thoracic CT
conference contribution
posted on 2015-01-01, 00:00 authored by Hailing Zhou, D Goldgof, S Hawkins, Lei WeiLei Wei, Y Liu, Douglas CreightonDouglas Creighton, R Gillies, L O Hall, Saeid NahavandiLung 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.
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Systems, Man, and Cybernetics. Conference (2015: Hong Kong, China)Series
IEEE International Conference on Systems Man and Cybernetics Conference ProceedingsPagination
2267 - 2272Publisher
IEEELocation
Hong Kong, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-10-09End date
2015-10-12ISSN
1062-922XLanguage
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and CyberneticsUsage metrics
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