A Slicing-Based Coherence Measure for Clusters of DTI Integral Curves
Abstract—We present a slicing-based coherence measure for clusters of
DTI integral curves. For a given cluster, we probe samples from the cluster
by slicing it with a plane at regularly spaced locations parametrized
by curve arc lengths. Then we compute a stability measure based on the
spatial relations between the projections of the curve points in individual
slices and their change across the slices. We demonstrate its use in
refining agglomerative hierarchical clustering results of DTI curves that
correspond to neural pathways. Expert evaluation shows that refinement
based on our measure can lead to improvement of clustering that is not
possible directly by using standard methods.