We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel 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
@article{arxiv.1404.0600,
title = {MBIS: Multivariate Bayesian Image Segmentation Tool},
author = {Oscar Esteban and Gert Wollny and Subrahmanyam Gorthi and Maria-J. Ledesma-Carbayo and Jean-Philippe Thiran and Andres Santos and Meritxell Bach-Cuadra},
journal= {arXiv preprint arXiv:1404.0600},
year = {2015}
}