MBIS: Multivariate Bayesian Image Segmentation Tool

Esteban Sanz-Dranguet, Oscar, Wöllny, Gert, Gorthi, Subrahmanyam, Ledesma Carbayo, María Jesús ORCID: https://orcid.org/0000-0001-6846-3923, Thiran, Jean-Philippe, 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.


Title: MBIS: Multivariate Bayesian Image Segmentation Tool
  • Esteban Sanz-Dranguet, Oscar
  • Wöllny, Gert
  • Gorthi, Subrahmanyam
  • Ledesma Carbayo, María Jesús https://orcid.org/0000-0001-6846-3923
  • Thiran, Jean-Philippe
  • Santos Lleo, Andrés
  • Bach-Cuadra, Meritxell
Item Type: Article
Título de Revista/Publicación: Computer Methods and Programs in Biomedicine
Date: July 2014
Volume: 115
Freetext Keywords: Multivariate; Reproducible research; image segmentation; graph-cuts; itk
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería Eléctrica [hasta 2014]
Creative Commons Licenses: None

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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 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.

More information

Item ID: 23383
DC Identifier: https://oa.upm.es/23383/
OAI Identifier: oai:oa.upm.es:23383
DOI: 10.1016/j.cmpb.2014.03.003
Deposited by: Oscar Esteban
Deposited on: 27 May 2014 13:09
Last Modified: 31 Jul 2015 22:56
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