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

Ellen Brunenberg; Remco Duits; Bart Ter Haar Romeny; Bram Platel

Conference ProceedingsOPEN ACCESS

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2010) 6361 LNCS(PART 1) 175-182

DOI: 10.1007/978-3-642-15705-9_22

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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. © 2010 Springer-Verlag.

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Brunenberg, E., Duits, R., Ter Haar Romeny, B., & Platel, B. (2010). A sobolev norm based distance measure for HARDI clustering: A feasibility study on phantom and real data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6361 LNCS, pp. 175–182). https://doi.org/10.1007/978-3-642-15705-9_22

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

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