You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
27 March 2009A comparison study of atlas-based image segmentation: the advantage of multi-atlas based on shape clustering
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.
The alert did not successfully save. Please try again later.
Xian Fan, Yiqiang Zhan, 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); https://doi.org/10.1117/12.814157