Knowledge-based segmentation and labeling of brain structures from MRI images

Jing-Hao Xue 1 Su Ruan 1 Bruno Moretti 1 Marinette Revenu 1 Daniel Bloyet 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : In this paper, we propose a new knowledge-based method illustrated in the context of segmentation, which labels internal brain structures viewed by magnetic resonance imaging (MRI). In order to improve the accuracy of the labeling, we introduce a fuzzy model of regions of interest (ROI) by analogy with the electrostatic potential distribution, to represent more appropriately the knowledge of distance, shape and relationship of structures. The knowledge is mainly derived from the Talairach stereotaxic atlas. The labeling is achieved by the regionwise labeling using genetic algorithms (GAs), followed by a voxelwise amendment using parallel region growing. The fuzzy model is used both to design the fitness function of GAs, and to guide the region growing. The performance of our proposed method has been quantitatively validated by six indices with respect to manually labeled images.
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Pattern Recognition Letters, Elsevier, 2001, 22 (3-4), pp.395-405. 〈10.1016/S0167-8655(00)00135-5〉
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https://hal.archives-ouvertes.fr/hal-00805970
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Soumis le : vendredi 29 mars 2013 - 11:54:30
Dernière modification le : jeudi 7 février 2019 - 17:47:27

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Jing-Hao Xue, Su Ruan, Bruno Moretti, Marinette Revenu, Daniel Bloyet. Knowledge-based segmentation and labeling of brain structures from MRI images. Pattern Recognition Letters, Elsevier, 2001, 22 (3-4), pp.395-405. 〈10.1016/S0167-8655(00)00135-5〉. 〈hal-00805970〉

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