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. Haar Romeny, ter, B. Platel

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

2 Citations (Scopus)

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 publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2010 (13th International Conference, Beijing, China, September 20-24, 2010. Proceedings, Part I)
EditorsT. Jiang, N. Navab, J.P.W. Pluim, M.A. Viergever
Place of PublicationBerlin
PublisherSpringer
Pages175-182
ISBN (Electronic)9783642157059
ISBN (Print)978-3-642-15704-2
DOIs
Publication statusPublished - 2010
Event13th International Conference on Medical Image Computing and Computer-Assisted Intervention (Miccai), September 20-24, 2010, Beijing, China - Beijing, China
Duration: 20 Sep 201024 Sep 2010

Publication series

NameLecture Notes in Computer Science
Volume6361
ISSN (Print)0302-9743

Conference

Conference13th International Conference on Medical Image Computing and Computer-Assisted Intervention (Miccai), September 20-24, 2010, Beijing, China
CountryChina
CityBeijing
Period20/09/1024/09/10

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