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Segmentation des tissus et structures sur les IRM cérébrales : agents markoviens locaux coopératifs et formulation bayésienne.

Benoît Scherrer 1
1 TIMC-PRETA - Physiologie cardio-Respiratoire Expérimentale Théorique et Appliquée
TIMC - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
Abstract : Accurate magnetic resonance brain scan segmentation is critical in a number of clinical and neuroscience applications. This task is challenging due to artifacts, low contrast between tissues and inter-individual variability that inhibit the introduction of a priori knowledge. In this thesis, we propose a new MR brain scan segmentation approach. Unique features of this approach include (1) the coupling of tissue segmentation, structure segmentation and prior knowledge construction, and (2) the consideration of local image properties.
Locality is modeled through a multi-agent framework: agents are distributed into the volume and perform a local Markovian segmentation. As an initial approach (LOCUS, Local Cooperative Unified Segmentation), intuitive cooperation and coupling mechanisms are proposed to ensure the consistency of local models. Structures are segmented via the introduction of spatial localization constraints based on fuzzy spatial relations between structures. In a second approach, (LOCUS-B, LOCUS in a Bayesian framework) we consider the introduction of a statistical atlas to describe structures. The problem is reformulated in a Bayesian framework, allowing a statistical formalization of coupling and cooperation. Tissue segmentation, local model regularization, structure segmentation and local affine atlas registration are then coupled in an EM framework and mutually improve.
The evaluation on simulated and real images shows good results, and in particular, a robustness to non-uniformity and noise with low computational cost. Local distributed and cooperative MRF models then appear as a powerful and promising approach for medical image segmentation.
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Contributor : Benoit Scherrer <>
Submitted on : Friday, February 13, 2009 - 4:54:08 PM
Last modification on : Monday, December 7, 2020 - 12:24:04 PM
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  • HAL Id : tel-00361317, version 1


Benoît Scherrer. Segmentation des tissus et structures sur les IRM cérébrales : agents markoviens locaux coopératifs et formulation bayésienne.. Modélisation et simulation. Institut National Polytechnique de Grenoble - INPG, 2008. Français. ⟨tel-00361317⟩



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