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Variational Approaches to the Estimation, Regularization and Segmentation of Diffusion Tensor Images

Rachid Deriche 1 David Tschumperlé 2 Christophe Lenglet 1, 3 Mikaël Rousson 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
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Diffusion magnetic resonance imaging probes and quantifies the anisotropic diffusion of water molecules in biological tissues, make it possible to non-invasively infer the architecture of the underlying structures. In this chapter, we present a set of new techniques for the robust estimation and regularization of diffusion tensor images (DTI) as well as a novel statistical framework for the segmentation of cerebral white matter structures from this type of dataset. Numerical experiments conducted on real diffusion weighted MRI illustrate the technique and exhibit promising results.
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Rachid Deriche, David Tschumperlé, Christophe Lenglet, Mikaël Rousson. Variational Approaches to the Estimation, Regularization and Segmentation of Diffusion Tensor Images. Paragios, Chen & Faugeras. Mathematical Models of Computer Vision: The Handbook, Springer, pp.517--530, 2005. ⟨hal-00336537⟩

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