![](/images/graphics-bg.png)
Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
Joint Authors
Gill, Gurman
Toews, Matthew
Beichel, Reinhard R.
Source
International Journal of Biomedical Imaging
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-21
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gill, Gurman& Toews, Matthew& Beichel, Reinhard R.. 2014. Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach. International Journal of Biomedical Imaging،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1036318
Modern Language Association (MLA)
Gill, Gurman…[et al.]. Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach. International Journal of Biomedical Imaging No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1036318
American Medical Association (AMA)
Gill, Gurman& Toews, Matthew& Beichel, Reinhard R.. Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach. International Journal of Biomedical Imaging. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1036318
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1036318