Abstract
We consider left-invariant diffusion processes on DTI data by embedding the data into the space \(\mathbb{R}^3\rtimes S^2\) of 3D positions and orientations. We then define and solve the diffusion equation in a moving frame of reference defined using left-invariant derivatives. The diffusion process is made adaptive to the data in order to do Perona-Malik-like edge preserving smoothing, which is necessary to handle fiber structures near regions of large isotropic diffusion such as the ventricles of the brain. The corresponding partial differential systems are solved using finite difference stencils. We include experiments both on synthetic data and on DTI-images of the brain.
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Creusen, E.J., Duits, R., Dela Haije, T.C.J. (2012). Numerical Schemes for Linear and Non-linear Enhancement of DW-MRI. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_2
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DOI: https://doi.org/10.1007/978-3-642-24785-9_2
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