Abstract | ||
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We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan–rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge. |
Year | DOI | Venue |
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2014 | 10.1016/j.cmpb.2014.03.003 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
62P10,62F15 | Journal | 115 |
Issue | ISSN | Citations |
2 | 0169-2607 | 1 |
PageRank | References | Authors |
0.37 | 55 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Oscar Esteban | 1 | 20 | 3.28 |
Gert Wollny | 2 | 57 | 9.08 |
Sai Subrahmanyam Gorthi | 3 | 10 | 2.54 |
María J. Ledesma-carbayo | 4 | 329 | 33.77 |
Jean-Philippe Thiran | 5 | 2320 | 257.56 |
A Santos | 6 | 281 | 26.52 |
Meritxell Bach Cuadra | 7 | 326 | 23.59 |