A sobolev norm based distance measure for HARDI clustering: a feasibility study on phantom and real data

Authors:
Ellen Brunenberg;Remco Duits;Bart ter Haar Romeny;Bram Platel
Affiliations:
Biomedical Engineering, Eindhoven University of Technology;Mathematics and Computer Science, Eindhoven University of Technology and Biomedical Engineering, Eindhoven University of Technology;Biomedical Engineering, Eindhoven University of Technology;Biomedical Engineering, Maastricht University Medical Center
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

Dissimilarity measures for DTI clustering are abundant. However, for HARDI, the L2 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 L2 norm.