MBIS: Multivariate Bayesian Image Segmentation Tool

Esteban Sanz-Dranguet, Oscar; Wöllny, Gert; Gorthi, Subrahmanyam; Ledesma Carbayo, María Jesús; Thiran, Jean-Philippe; Santos Lleo, Andrés y 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.

Descripción

Título: MBIS: Multivariate Bayesian Image Segmentation Tool
Autor/es:
  • Esteban Sanz-Dranguet, Oscar
  • Wöllny, Gert
  • Gorthi, Subrahmanyam
  • Ledesma Carbayo, María Jesús
  • Thiran, Jean-Philippe
  • Santos Lleo, Andrés
  • Bach-Cuadra, Meritxell
Tipo de Documento: Artículo
Título de Revista/Publicación: Computer Methods and Programs in Biomedicine
Fecha: Julio 2014
Volumen: 115
Materias:
Palabras Clave Informales: Multivariate; Reproducible research; image segmentation; graph-cuts; itk
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Ingeniería Eléctrica [hasta 2014]
Licencias Creative Commons: Ninguna

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Resumen

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.

Más información

ID de Registro: 23383
Identificador DC: http://oa.upm.es/23383/
Identificador OAI: oai:oa.upm.es:23383
Identificador DOI: 10.1016/j.cmpb.2014.03.003
Depositado por: Oscar Esteban
Depositado el: 27 May 2014 13:09
Ultima Modificación: 31 Jul 2015 22:56
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