Segmentation of 3D probability density fields by surface evolution: Application to diffusion MRI

Christophe Lenglet, Mikaël Rousson, Rachid Deriche

Research output: Contribution to journalConference articlepeer-review

24 Scopus citations

Abstract

We propose an original approach for the segmentation of three-dimensional fields of probability density functions. This presents a wide range of applications in medical images processing, in particular for diffusion magnetic resonance imaging where each voxel is assigned with a function describing the average motion of water molecules. Being able to automatically extract relevant anatomical structures of the white matter, such as the corpus callosum, would dramatically improve our current knowledge of the cerebral connectivity as well as allow for their statistical analysis. Our approach relies on the use of the symmetrized Kullback-Leibler distance and on the modelization of its distribution over the subsets of interest in the volume. The variational formulation of the problem yields a level-set evolution converging toward the optimal segmentation.

Original languageEnglish (US)
Pages (from-to)18-25
Number of pages8
JournalLecture Notes in Computer Science
Volume3216
Issue numberPART 1
DOIs
StatePublished - 2004
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France
Duration: Sep 26 2004Sep 29 2004

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