Bayesian Markov model for cooperative clustering: application to robust MRI brain scan segmentation

Florence Forbes 1 Scherrer Benoît 2 Dojat Michel 3, *
* Corresponding author
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIN - Grenoble Institut des Neurosciences
3 ANTE-INSERM U836, équipe 5 - Neuro-imagerie fonctionnelle et métabolique
GIN - Grenoble Institut des Neurosciences
Abstract : Clustering is a fundamental data analysis step that consists of producing a partition of the observations to account for the groups existing in the observed data. In this paper, we introduce an additional cooperative aspect. We address cases in which the goal is to produce not a single partition but two or more possibly related partitions using cooperation between them. Cooperation is expressed by assuming the existence of two sets of labels (group assignments) which are not independent. We also model additional interactions by considering dependencies between labels within each label set. We propose then a cooperative setting formulated in terms of conditional Markov Random Field models for which we provide alternating and cooperative estimation procedures based on variants of the Expectation Maximization (EM) algorithm for inference. We illustrate the advantages of our approach by showing its ability to deal successfully with the complex task of segmenting simultaneously and cooperatively tissues and structures from MRI brain scans.
Keywords : Model-based clustering Markov random fields Bayesian analysis EM algorithm Generalized alternating maximization Human Brain
Document type :
Journal articles
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download
https://www.hal.inserm.fr/inserm-00752896
Contributor : Michel Dojat <>
Submitted on : Friday, November 16, 2012 - 3:56:49 PM
Last modification on : Wednesday, April 11, 2018 - 1:59:47 AM
Long-term archiving on : Saturday, December 17, 2016 - 11:40:31 AM

File

Forbes_2011_Bayesian_MA.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inserm-00752896, version 1

Collections

INRIA | U836 | CNRS | LJK_PS_MISTIS | LJK_PS | LJK | UGA

Citation

Florence Forbes, Scherrer Benoît, Dojat Michel. Bayesian Markov model for cooperative clustering: application to robust MRI brain scan segmentation. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2011, 152 (3), pp.116-141. ⟨inserm-00752896⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

726

Files downloads

346

Contact

CCSD