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Journal of Electronic Imaging

Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data
Author(s): Leonid Zhukov; Ken Museth; David E. Breen; Ross T. Whitaker; Alan H. Barr
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Paper Abstract

Segmentation of anatomical regions of the brain is one of the fundamental problems in medical image analysis. It is traditionally solved by iso-surfacing or through the use of active contours/deformable models on a gray-scale magnetic resonance imaging (MRI) data. We develop a technique that uses anisotropic diffusion properties of brain tissue available from diffusion tensor (DT)-MRI to segment brain structures. We develop a computational pipeline starting from raw diffusion tensor data through computation of invariant anisotropy measures to construction of geometric models of the brain structures. This provides an environment for user-controlled 3-D segmentation of DT-MRI datasets. We use a level set approach to remove noise from the data and to produce smooth, geometric models. We apply our technique to DT-MRI data of a human subject and build models of the isotropic and strongly anisotropic regions of the brain. Once geometric models have been constructed they can be combined to study spatial relationships and quantitatively analyzed to produce the volume and surface area of the segmented regions.

Paper Details

Date Published: 1 January 2003
PDF: 9 pages
J. Electron. Imaging. 12(1) doi: 10.1117/1.1527628
Published in: Journal of Electronic Imaging Volume 12, Issue 1
Show Author Affiliations
Leonid Zhukov, California Institute of Technology (United States)
Ken Museth, California Institute of Technology (United States)
David E. Breen, California Institute of Technology (United States)
Ross T. Whitaker, Univ. of Utah (United States)
Alan H. Barr, California Institute of Technology (United States)


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