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Abstract

While isosurfaces of anisotropy measures for data from diffusion
tensor magnetic resonance imaging (DT-MRI) are known to depict major
anatomical structures, the anisotropy metric reduces the rich tensor
data to a simple scalar field. In this work, we suggest that the
part of the data which has been ignored by the metric can be used to
segment anisotropy isosurfaces into anatomically meaningful regions.
For the implementation, we propose an edge-based watershed method
that adapts and extends a method from curvature-based mesh
segmentation. Finally, we use the segmentation
results to enhance visualization of the data.