MRF agent based segmentation: Application to MRI brain scans

Benoit Scherrer 1 Dojat Michel 1, * Florence Forbes 2 Catherine Garbay 3
* Auteur correspondant
1 GIN - Grenoble Institut des Neurosciences
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 LIG Laboratoire d'Informatique de Grenoble - ADELE
LIG - Laboratoire d'Informatique de Grenoble
Abstract : The Markov Random Field (MRF) probabilistic framework is classically introduced for a robust segmentation of Magnetic Resonance Imaging (MRI) brain scans. Most MRF approaches handle tissues segmentation via global model estimation. Structure segmentation is then carried out as a separate task. We propose in this paper to consider MRF segmentation of tissues and structures as two local and cooperative procedures immersed in a multiagent framework. Tissue segmentation is performed by partitionning the volume in subvolumes where agents estimate local MRF models in cooperation with their neighbours to ensure consistency of local models. These models better reflect local intensity distributions. Structure segmentation is performed via dynamically localized agents that integrate anatomical spatial constraints provided by an a priori fuzzy description of brain anatomy. Structure segmentation is not reduced to a postprocessing step: rather, structure agents cooperate with tissue agents to render models gradually more accurate. We report several experiments that illustrate the working of our multiagent framework. The evaluation was performed using both phantoms and real 3T brain scans and showed a robustness to nonuniformity and noise together with a low computational time. This MRF agent based approach appears as a very promising new tool for complex image segmentation.
Type de document :
Communication dans un congrès
Bellazzi R; Abu-Hanna A; Hunter J. AIME 2007 - 11th Conference on Artificial Intelligence in Medicine, Jul 2007, Amsterdam, Netherlands. Springer, 4594, pp.13-23, 2007, Lecture Notes in Computer Science. 〈10.1007/978-3-540-73599-1_2〉
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger
http://www.hal.inserm.fr/inserm-00519836
Contributeur : Michel Dojat <>
Soumis le : mercredi 8 juin 2011 - 20:30:05
Dernière modification le : jeudi 11 octobre 2018 - 08:48:03
Document(s) archivé(s) le : vendredi 9 septembre 2011 - 12:02:19

Fichier

AIME2007_locus.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | U836 | LJK_PS | LJK_PS_MISTIS | LJK | UGA | LIG | LIG_SRCPR

Citation

Benoit Scherrer, Dojat Michel, Florence Forbes, Catherine Garbay. MRF agent based segmentation: Application to MRI brain scans. Bellazzi R; Abu-Hanna A; Hunter J. AIME 2007 - 11th Conference on Artificial Intelligence in Medicine, Jul 2007, Amsterdam, Netherlands. Springer, 4594, pp.13-23, 2007, Lecture Notes in Computer Science. 〈10.1007/978-3-540-73599-1_2〉. 〈inserm-00519836〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

556

Téléchargements de fichiers

168

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD