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

Ingrid C. Sluimer; Meindert Niemeijer; Bram van Ginneken

doi:10.1117/12.535312

12 May 2004 Lung field segmentation from thin-slice CT scans in presence of severe pathology

Ingrid C. Sluimer, Meindert Niemeijer, Bram van Ginneken

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535312
Event: Medical Imaging 2004, 2004, San Diego, California, United States

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.

Citation Download Citation

Ingrid C. Sluimer, Meindert Niemeijer, and Bram van Ginneken "Lung field segmentation from thin-slice CT scans in presence of severe pathology", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535312

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available