A Model-Free Unsupervised Method to Cluster Brain Tissue Directly From DWI Volumes
Matthew Liptrot 1 and Franois Lauze 1
Department of Computer Science, University
of Copenhagen, Copenhagen, Copenhagen, Denmark
We present a simple, novel approach to the voxelwise
classification of brain tissue acquired with
diffusion-weighted imaging (DWI). By working directly
upon the individual DWI volume data, it makes no
assumption of an underlying diffusion model. In
addition, by summarising statistics across the diffusion
gradient directions, we obtain features that are
rotationally invariant. We show an example of how well a
resulting cluster spatially matches a high FA region,
thereby corresponding to probable single-tract voxels.
The method could have application during tractography
pre-processing, and has potential as a complementary
approach for analysis of DWI datasets.
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