Abstract
Dissimilarity measures for DTI clustering are abundant. However, for HARDI, the \(\mathbb{L}_{2}\) 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 \(\mathbb{L}_{2}\) norm.
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Brunenberg, E., Duits, R., ter Haar Romeny, B., Platel, B. (2010). A Sobolev Norm Based Distance Measure for HARDI Clustering. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_22
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DOI: https://doi.org/10.1007/978-3-642-15705-9_22
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