Estimation of the prior distribution of ground truth in the STAPLE algorithm: an empirical bayesian approach
Pages 593 - 600
Abstract
We present a new fusion algorithm for the segmentation and parcellation of magnetic resonance (MR) images of the brain. Our algorithm is a parametric empirical Bayesian extension of the STAPLE algorithm which uses the observations to accurately estimate the prior distribution of the hidden ground truth using an expectation maximization (EM) algorithm. We use IBSR dataset for the evaluation of our fusion algorithm. We segment 128 principle gray and white matter structures of the brain using our novel method and eight other state-of-the-art algorithms in the literature. Our prior distribution estimation strategy improves the accuracy of the fusion algorithm. It was shown that our new fusion algorithm has superior performance compared to the other state-of-the-art fusion methods in the literature.
References
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Published In
October 2012
746 pages
ISBN:9783642334146
Sponsors
- ERC MedYMA: ERC MedYMA
- Canon Median: Canon Median
- Siemens
- GE HEALTHCARE: GE Healthcare
- Philips: Philips
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 October 2012
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