A Sobolev Norm Based Distance Measure for HARDI Clustering A Feasibility Study on Phantom and Real Data

摘要

Dissimilarity measures for DTI clustering are abundant. However, for HARDI, the L_2 norm has up to now been one of only few practically feasible measures. In this paper we propose a new measure, that not only compares the amplitude of diffusion profiles, but also rewards coincidence of the extrema. We tested this on phantom and real brain data. In both cases, our measure significantly outperformed the L_2 norm.