  Brain fiber tracts are widely used in studying brain diseases, which may lead
to a better understanding of how disease affects the brain. The segmentation of
brain fiber tracts assumed enormous importance in disease analysis. In this
paper, we propose a novel vector field streamline clustering framework for
brain fiber tract segmentations. Brain fiber tracts are firstly expressed in a
vector field and compressed using the streamline simplification algorithm.
After streamline normalization and regular-polyhedron projection,
high-dimensional features of each fiber tract are computed and fed to the IDEC
clustering algorithm. We also provide qualitative and quantitative evaluations
of the IDEC clustering method and QB clustering method. Our clustering results
of the brain fiber tracts help researchers gain perception of the brain
structure. This work has the potential to automatically create a robust fiber
bundle template that can effectively segment brain fiber tracts while enabling
consistent anatomical tract identification.
