Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data
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.
Additional Information
© 2003 SPIE and IS&T. Paper MIP-13 received May 1, 2001; revised manuscript received Oct. 1, 2001; accepted for publication Mar. 1, 2002. We would like to thank Dr. J. Michael Tyszka, Dr. Miriam Scadeng, and Dr. David Dubowitz for helping us to identify the 3-D structures extracted from the DT dataset. Dr. Jason Wood developed the Iris Explorer modules used to produce part of the results in the paper. This work was supported by National Science Foundation (NSF) Grants No. ACI-9982273 and No. ASC-89-20219, the National Institute on Drug Abuse, the National Institute of Mental Health, and the NSF, as part of the Human Brain Project, Office of Naval Research Volume Visualization Grant No. N000140110033, and the National Library of Medicine "Insight" Project No. N01-LM-0-3503. The first DT-MRI dataset is courtesy of the University of Utah SCI Institute, the second dataset is courtesy of Dr. Mark Bastin, University of Edinburgh, United Kingdom. Finally, we would like to thank our reviewers for a very detailed review and multiple valuable suggestions.Attached Files
Published - 125_1.pdf
Files
Name | Size | Download all |
---|---|---|
md5:2b87fa6e7fb168c654dde595ae23386b
|
977.4 kB | Preview Download |
Additional details
- Eprint ID
- 71460
- Resolver ID
- CaltechAUTHORS:20161025-130839144
- NSF
- ACI-9982273
- NSF
- ASC-89-20219
- National Institute on Drug Abuse
- National Institute of Mental Health (NIMH)
- NSF
- Office of Naval Research (ONR)
- N000140110033
- National Library of Medicine
- N01-LM-0-3503
- Created
-
2016-10-25Created from EPrint's datestamp field
- Updated
-
2021-11-11Created from EPrint's last_modified field