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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.
Originele taal-2Engels
TitelProceedings of the Sixteenth Annula Conference of the Advanced School for Computings and Imaging, Veldhoven, The Netherlands, November 1-3, 2010
RedacteurenT. Kielmann, M.J. van Kreveld, W.J. Niessen
UitgeverijAdvanced School for Computing and Imaging (ASCI)
Aantal pagina's7
ISBN van geprinte versie978-90-79982-08-0
StatusGepubliceerd - 2010
Evenement16th Annual Conference of the Advanced School for Computing and Imaging (ASCI 2010), June 1-3, 2010, Veldhoven, The Netherlands - Veldhoven, Nederland
Duur: 1 jun 20103 jun 2010

Congres

Congres16th Annual Conference of the Advanced School for Computing and Imaging (ASCI 2010), June 1-3, 2010, Veldhoven, The Netherlands
Verkorte titelASCI 2010
LandNederland
StadVeldhoven
Periode1/06/103/06/10

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  • Citeer dit

    Brunenberg, E. J. L., Duits, R., ter Haar Romeny, B. M., & Platel, B. (2010). A Sobolev norm based distance measure for HARDI clustering : a feasibility study on phantom and real data. In T. Kielmann, M. J. van Kreveld, & W. J. Niessen (editors), Proceedings of the Sixteenth Annula Conference of the Advanced School for Computings and Imaging, Veldhoven, The Netherlands, November 1-3, 2010 Advanced School for Computing and Imaging (ASCI).