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

E.J.L. Brunenberg, R. Duits, B.M. ter Haar Romeny, B. Platel

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the Sixteenth Annula Conference of the Advanced School for Computings and Imaging, Veldhoven, The Netherlands, November 1-3, 2010
EditorsT. Kielmann, M.J. van Kreveld, W.J. Niessen
PublisherAdvanced School for Computing and Imaging (ASCI)
Number of pages7
ISBN (Print)978-90-79982-08-0
Publication statusPublished - 2010
Event16th Annual Conference of the Advanced School for Computing and Imaging (ASCI 2010), June 1-3, 2010, Veldhoven, The Netherlands - Veldhoven, Netherlands
Duration: 1 Jun 20103 Jun 2010

Conference

Conference16th Annual Conference of the Advanced School for Computing and Imaging (ASCI 2010), June 1-3, 2010, Veldhoven, The Netherlands
Abbreviated titleASCI 2010
CountryNetherlands
CityVeldhoven
Period1/06/103/06/10

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