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Simultaneous Manifold Learning and Clustering: Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas

Demian Wassermann 1 Rachid Deriche 1
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : We propose a new clustering algorithm. This algorithm performs clustering and manifold learning simultaneously by using a graph-theoretical approach to manifold learning. We apply this algorithm in order to cluster white matter fiber tracts obtained from Diffusion Tensor MRI (DT-MRI) through streamline tractography. Our algorithm is able perform clustering of these fiber tracts incorporating information about the shape of the fiber and a priori knowledge as the probability of the fiber belonging to known anatomical structures. This anatomical knowledge is incorporated as a volumetric white matter atlas, in this case LONI's ICBM DTI-81
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https://hal.inria.fr/inria-00430186
Contributor : Alain Monteil <>
Submitted on : Friday, November 6, 2009 - 9:15:56 AM
Last modification on : Tuesday, September 22, 2020 - 3:50:12 AM

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  • HAL Id : inria-00430186, version 1

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Demian Wassermann, Rachid Deriche. Simultaneous Manifold Learning and Clustering: Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas. MICCAI 2008 Workshop - Manifolds in Medical Imaging: Metrics, Learning and Beyond, Oct 2008, New-York, United States. ⟨inria-00430186⟩

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