SPECIAL SECTION ON MODEL-BASED MEDICAL IMAGE PROCESSING AND ANALYSIS Features

Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data

[+] Author Affiliations
Leonid Zhukov, Ken Museth, David Breen, Alan H. Barr

California Institute of Technology, Department of Computer Science, Mail Code 350-74, Pasadena, California?91125-7400 E-mail: {zhukov,kmu,david,barr}@gg.caltech.edu

Ross Whitaker

University of Utah, School of Computing, 3190 MEB, Salt Lake City, Utah?84112-9205

J. Electron. Imaging. 12(1), 125-133 (Jan 01, 2003). doi:10.1117/1.1527628
History: Received May 1, 2001; Revised Oct. 1, 2001; Accepted Mar. 1, 2002; Online January 29, 2003
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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. © 2003 SPIE and IS&T.

© 2003 SPIE and IS&T

Citation

Leonid Zhukov ; Ken Museth ; David Breen ; Alan H. Barr and Ross Whitaker
"Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data", J. Electron. Imaging. 12(1), 125-133 (Jan 01, 2003). ; http://dx.doi.org/10.1117/1.1527628


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