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27 March 2009 A comparison study of atlas-based image segmentation: the advantage of multi-atlas based on shape clustering
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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725919 (2009)
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
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.
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Xian Fan, Yiqiang Zhan, and Gerardo Hermosillo Valadez "A comparison study of atlas-based image segmentation: the advantage of multi-atlas based on shape clustering", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725919 (27 March 2009);

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