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Proceedings Paper

Lung field segmentation from thin-slice CT scans in presence of severe pathology
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Paper Abstract

Conventional methods for the segmentation of lung fields from thorax CT scans are based on thresholding. They rely on a large grey value contrast between the lung parenchyma and surrounding tissues. In the presence of consolidations or other high density pathologies, these methods fail. For the segmentation of such scans, a lung shape should be induced without relying solely on grey level information. We present a segmentation-by-registration approach to segment the lung fields from several thin-slice CT scans (slice-thickness 1 mm) containing high density pathologies. A scan of a normal subject is elastically registered to each of the abnormal scans. Applying the found deformations to a lung mask created for the normal subject, a segmentation of the abnormal lungs is found. We implemented a conventional lung field segmentation method and compared it to the one using non-rigid registration techniques. The results of the algorithms were evaluated against manual segmentations in several slices of each scan. It is shown that the segmentation-by-registration approach can successfully identify the lung regions where the conventional method fails.

Paper Details

Date Published: 12 May 2004
PDF: 9 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535312
Show Author Affiliations
Ingrid C. Sluimer, Univ. Medical Ctr. Utrecht (Netherlands)
Meindert Niemeijer, Univ. Medical Ctr. Utrecht (Netherlands)
Bram van Ginneken, Univ. Medical Ctr. Utrecht (Netherlands)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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