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International Journal of Biomedical Imaging
Volume 2014, Article ID 479154, 7 pages
http://dx.doi.org/10.1155/2014/479154
Research Article

Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach

1Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA
2The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA
3Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
4Department of Internal Medicine, The University of Iowa, Iowa City, IA 52242, USA

Received 10 July 2014; Accepted 2 October 2014; Published 21 October 2014

Academic Editor: Guowei Wei

Copyright © 2014 Gurman Gill et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.