Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection.
Publication year
2009Source
Medical Physics, 36, 7, (2009), pp. 2934-47ISSN
Publication type
Article / Letter to editor
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Organization
Radiology
Journal title
Medical Physics
Volume
vol. 36
Issue
iss. 7
Page start
p. 2934
Page end
p. 47
Subject
ONCOL 5: Aetiology, screening and detectionAbstract
Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical data. Some methods to handle these situations have been proposed, but they are too time consuming or too specialized to be used in clinical practice. In this article, a new hybrid lung segmentation method is presented that automatically detects failures of a conventional algorithm and, when needed, resorts to a more complex algorithm, which is expected to produce better results in abnormal cases. In a large quantitative evaluation on a database of 150 scans from different sources, the hybrid method is shown to perform substantially better than a conventional approach at a relatively low increase in computational cost.
This item appears in the following Collection(s)
- Academic publications [241901]
- Electronic publications [127360]
- Faculty of Medical Sciences [91954]
- Open Access publications [102119]
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