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Conference Papers Year : 2008

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

Demian Wassermann
Rachid Deriche
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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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Medical Imaging
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Dates and versions

inria-00430186 , version 1 (06-11-2009)

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  • HAL Id : inria-00430186 , version 1

Cite

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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