Abstract
We present a novel structure tensor for matrix-valued images. It allows for user defined parameters that add flexibility to a number of image processing algorithms for the segmentation and smoothing of tensor fields. We provide a thorough theoretical derivation of the new structure tensor, including a proof of the equivalence of its unweighted version to the existing structure tensor from the literature. Finally, we demonstrate its advantages for segmentation and smoothing, both on synthetic tensor fields and on real DT-MRI data.
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Schultz, T., Burgeth, B., Weickert, J. (2006). Flexible Segmentation and Smoothing of DT-MRI Fields Through a Customizable Structure Tensor. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_46
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DOI: https://doi.org/10.1007/11919476_46
Publisher Name: Springer, Berlin, Heidelberg
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