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Optimisation de séquences de segmentation combinant modèle structurel et focalisation de l'attention visuelle. Application à la reconnaissance de structures cérébrales dans des images 3D.

Abstract : We aim at recognizing a 3D scene described by a 3D image and a structural model, i.e., a model that describes the spatial arrangement of the objects. The sequential segmentation framework is considered. This allows us to segment and recognize objects in a sequential way, using at each step the previously recognized object to guide the segmentation of the next ones. We propose to use the spatial information included in the model to optimize the segmentation sequence from a reference object to a selected target. This sequence is viewed as a path in a graph where a node represents an object and an edge carries the spatial relation information between two objects. We propose to use the spatial information included in the model to optimized the segmentation sequence from a reference object to a selected target. This sequence is view as a path in a graph where vertex represents objects and edges represents spatial relations. Two approaches are proposed. The first one proposes to evaluate the relevance of a path according to the generic available knowledge. This estimation is realized either on each spatial relation independently or directly on a fuzzy subset that represents the whole path at once. The best path according to a criterion is then selected and the objects may be segmented. The second approache proposes to integrate the segmentation sequence optimization directly into a sequential segmentation framework. The optimization uses a spatial model of the scene modeled as a graph and also a saliency map to guide the segmentation. The latter can be seen as an image exploration process. Both approaches are used for segmentation and recognition of internal brain structures in 3D magnetic resonance images. We also propose an adaptation of these methods to cope with pathological cases (e.g., brain tumors).
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https://pastel.archives-ouvertes.fr/pastel-00006074
Contributor : Ecole Télécom Paristech <>
Submitted on : Wednesday, May 19, 2010 - 8:00:00 AM
Last modification on : Friday, July 31, 2020 - 10:44:07 AM
Long-term archiving on: : Thursday, March 30, 2017 - 5:52:18 AM

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  • HAL Id : pastel-00006074, version 1

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Geoffroy Fouquier. Optimisation de séquences de segmentation combinant modèle structurel et focalisation de l'attention visuelle. Application à la reconnaissance de structures cérébrales dans des images 3D.. domain_other. Télécom ParisTech, 2010. Français. ⟨pastel-00006074⟩

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