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MRF Agent Based Segmentation: Application to MRI Brain Scans

  • B. Scherrer
  • M. Dojat
  • F. Forbes
  • C. Garbay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4594)

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 apriori 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.

Keywords

Model Check Magnetic Resonance Image Brain Markov Random Field Territory Size Tissue Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • B. Scherrer
    • 1
    • 2
  • M. Dojat
    • 1
  • F. Forbes
    • 3
  • C. Garbay
    • 2
  1. 1.INSERM U836-UJF-CEA-CHU (Grenoble Institute of Neuroscience) 
  2. 2.CNRS UMR 5217, LIG (Laboratoire d’Informatique de Grenoble) MAGMA 
  3. 3.INRIA, Laboratoire Jean Kuntzmann, Universite de Grenoble (MISTIS) 

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