Inference of a HARDI fiber bundle atlas using a two-level clustering strategy.

Abstract : This paper presents a method inferring a model of the brain white matter organisation from HARDI tractography results computed for a group of subjects. This model is made up of a set of generic fiber bundles that can be detected in most of the population. Our approach is based on a two-level clustering strategy. The first level is a multiresolution intra-subject clustering of the million tracts that are computed for each brain. This analysis reduces the complexity of the data to a few thousands fiber bundles for each subject. The second level is an intersubject clustering over fiber bundle centroids from all the subjects using a pairwise distance computed after spatial normalization. The resulting model includes the large bundles of anatomical literature and about 20 U-fiber bundles in each hemisphere.
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Medical image computing and computer-assisted intervention : MICCAI .. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2010, 13 (Pt 1), pp.550-7
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https://hal.inria.fr/inria-00541944
Contributeur : Pierre Fillard <>
Soumis le : mercredi 1 décembre 2010 - 15:12:06
Dernière modification le : vendredi 22 juin 2018 - 01:20:19

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  • HAL Id : inria-00541944, version 1
  • PUBMED : 20879274

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Pamela Guevara, Cyril Poupon, Denis Rivière, Yann Cointepas, Linda Marrakchi, et al.. Inference of a HARDI fiber bundle atlas using a two-level clustering strategy.. Medical image computing and computer-assisted intervention : MICCAI .. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2010, 13 (Pt 1), pp.550-7. 〈inria-00541944〉

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