Variational approaches to the estimation, regularizatinn and segmentation of diffusion tensor images

R. Deriche, D. Tschumpelé, C. Lenglet, M. Rousson

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

Diffusion magnetic resonance imaging probes and quantifies the anisotropic diffusion of water molecules in biological tissues, making it possible to non-invasively infer the architecture of the underlying structure. 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 techniques and exhibit promising results.

Original languageEnglish (US)
Title of host publicationHandbook of Mathematical Models in Computer Vision
PublisherSpringer US
Pages517-530
Number of pages14
ISBN (Print)0387263713, 9780387263717
DOIs
StatePublished - Dec 1 2006

Fingerprint Dive into the research topics of 'Variational approaches to the estimation, regularizatinn and segmentation of diffusion tensor images'. Together they form a unique fingerprint.

  • Cite this

    Deriche, R., Tschumpelé, D., Lenglet, C., & Rousson, M. (2006). Variational approaches to the estimation, regularizatinn and segmentation of diffusion tensor images. In Handbook of Mathematical Models in Computer Vision (pp. 517-530). Springer US. https://doi.org/10.1007/0-387-28831-7_32