MBIS: Multivariate Bayesian Image Segmentation Tool

• Published in: arXiv.org
• Main Authors: Esteban, Oscar, Wollny, Gert, Subrahmanyam Gorthi, Maria-J Ledesma-Carbayo, Thiran, Jean-Philippe, Santos, Andres, Bach-Cuadra, Meritxell
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 07.04.2014
• Subjects: Bayesian analysis; Bias; Clustering; Computer Science - Computer Vision and Pattern Recognition; Image segmentation; Markov analysis; Markov chains; Optimization
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1404.0600

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