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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.