A Similarity Model and Segmentation Algorithm for White Matter Fiber Tracts

Recently, fiber segmentation has become an
emerging technique in neuroscience. Grouping fiber tracts into
anatomical meaningful bundles allows to study the structure
of the brain and to investigate onset and progression of neurodegenerative and mental diseases. In this paper, we propose
a novel technique for fiber tracts based on shape similarity
and connection similarity. For shape similarity, we propose
some new techniques adapted from existing similarity measures
for trajectory data. We also propose a new technique called
Warped Longest Common Subsequence (WLCS) for which
we additionally developed a lower-bounding distance function
to speed up the segmentation process. Our segmentation is
based on an outlier-robust density-based clustering algorithm.
Extensive experiments on diffusion tensor images demonstrate
the efficiency and effectiveness of our technique.