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Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection
Author(s) -
van Rikxoort Eva M.,
de Hoop Bartjan,
Viergever Max A.,
Prokop Mathias,
van Ginneken Bram
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3147146
Subject(s) - segmentation , computer science , computed tomography , artificial intelligence , lung , image segmentation , pattern recognition (psychology) , computer vision , attenuation , radiology , medicine , physics , optics
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.