Next: Introduction
Level Set Modeling and Segmentation of DT-MRI Brain Data
Leonid Zhukov, Ken Museth, David Breen, Ross Whitakert, Alan H. Barr
Department of Computer Science,California Institute of Technology
Mail Code 350-74, Pasadena, CA 91125-7400
School of Computing, University of Utah
3190 MEB, Salt Lake City, UT 84112-9205
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 MRI data. In this paper we
develop a technique that uses anisotropic diffusion properties of
brain tissue available from DT-MRI to segment out 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 3D 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 may be combined to study spatial relationships
and quantitatively analyzed to produce the volume and surface area of
the segmented regions.
Next: Introduction
Leonid Zhukov
2003-09-14