1. MBIS: multivariate Bayesian image segmentation tool.
- Author
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Esteban O, Wollny G, Gorthi S, Ledesma-Carbayo MJ, Thiran JP, Santos A, and Bach-Cuadra M
- Subjects
- Adult, Aged, Aged, 80 and over, Aging pathology, Algorithms, Brain pathology, Cluster Analysis, Humans, Markov Chains, Middle Aged, Models, Statistical, Multivariate Analysis, Organ Size, Software, Young Adult, Bayes Theorem, Brain anatomy & histology, Image Interpretation, Computer-Assisted methods, Magnetic Resonance Imaging statistics & numerical data
- Abstract
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., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)
- Published
- 2014
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