Citation
Esteban Sanz-Dranguet, Oscar and Wöllny, Gert and Gorthi, Subrahmanyam and Ledesma Carbayo, María Jesús and Thiran, Jean-Philippe and Santos Lleo, Andrés and Bach-Cuadra, Meritxell
(2014).
MBIS: Multivariate Bayesian Image Segmentation Tool.
"Computer Methods and Programs in Biomedicine", v. 115
(n. 2);
pp. 76-94.
https://doi.org/10.1016/j.cmpb.2014.03.003.
Abstract
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.
