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Proceedings Paper

A comparison study of atlas-based image segmentation: the advantage of multi-atlas based on shape clustering
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Paper Abstract

Purpose: By incorporating high-level shape priors, atlas-based segmentation has achieved tremendous success in the area of medical image analysis. However, the effect of various kinds of atlases, e.g., average shape model, example-based multi-atlas, has not been fully explored. In this study, we aim to generate different atlases and compare their performance in segmentation. Methods: We compare segmentation performance using parametric deformable model with four different atlases, including 1) a single atlas, i.e., average shape model (SAS); 2) example-based multi-atlas (EMA); 3) cluster-based average shape models (CAS); 4) cluster-based statistical shape models (average shape + principal shape variation modes)(CSS). CAS and CSS are novel atlases constructed by shape clustering. For comparison purpose, we also use PDM without atlas (NOA) as a benchmark method. Experiments: The experiment is carried on liver segmentation from whole-body CT images. Atlases are constructed by 39 manually delineated liver surfaces. 11 CT scans with ground truth are used as testing data set. Segmentation accuracy using different atlases are compared. Conclusion: Compared with segmentation without atlas, all of the four atlas-based image segmentation methods achieve better results. Multi-atlas based segmentation behaves better than single-atlas based segmentation. CAS exhibit superior performance to all other methods.

Paper Details

Date Published: 27 March 2009
PDF: 7 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725919 (27 March 2009); doi: 10.1117/12.814157
Show Author Affiliations
Xian Fan, Johns Hopkins Univ. (United States)
Siemens Medical Solutions USA, Inc. (United States)
Yiqiang Zhan, Siemens Medical Solutions USA, Inc. (United States)
Gerardo Hermosillo Valadez, Siemens Medical Solutions USA, Inc. (United States)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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