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Structural Adaptive Smoothing: Principles and Applications in Imaging

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Mathematical Methods for Signal and Image Analysis and Representation

Part of the book series: Computational Imaging and Vision ((CIVI,volume 41))

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Abstract

Structural adaptive smoothing provides a new concept of edge-preserving non-parametric smoothing methods. In imaging it employs qualitative assumption on the underlying homogeneity structure of the image. The chapter describes the main principles of the approach and discusses applications ranging from image denoising to the analysis of functional and diffusion weighted Magnetic Resonance experiments.

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Acknowledgements

This work is supported by the DFG Research Center Matheon. It was made possible in part by software and a dataset from the NIH/NCRR Center for Integrative Biomedical Computing, P41-RR12553. The authors would like to thank A. Anwander at the Max Planck Institute for Human Cognitive and Brain Sciences (Leipzig, Germany) and H.U. Voss at the Citigroup Biomedical Imaging Center, Weill Cornell Medical College for providing diffusion-weighted and functional MR datasets. Furthermore, the authors would like to thank H.U. Voss for numerous intense and helpful discussions on Magnetic Resonance Imaging and related issues.

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  1. Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstr. 39, 10117, Berlin, Germany

    Jörg Polzehl & Karsten Tabelow

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  1. Jörg Polzehl
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  2. Karsten Tabelow
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Correspondence to Jörg Polzehl .

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Editors and Affiliations

  1. Dept. Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, 5600 MB, The Netherlands

    Luc Florack

  2. Dept. Mathematics & Computer Science, Eindhoven University of Technology, P.O. Box 513, Eindhoven, 5600 MB, The Netherlands

    Remco Duits

  3. Dept. Applied Mathematics, Delft University of Technology, Delft, 2628 CD, The Netherlands

    Geurt Jongbloed

  4. Probability & Stochastic Networks (PNA), Centrum Wiskunde & Informatica, Amsterdam, 1098 XG, The Netherlands

    Marie-Colette van Lieshout

  5. Fakultät für Mathematik, Universität Duisburg-Essen, Essen, 45141, Germany

    Laurie Davies

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Polzehl, J., Tabelow, K. (2012). Structural Adaptive Smoothing: Principles and Applications in Imaging. In: Florack, L., Duits, R., Jongbloed, G., van Lieshout, MC., Davies, L. (eds) Mathematical Methods for Signal and Image Analysis and Representation. Computational Imaging and Vision, vol 41. Springer, London. https://doi.org/10.1007/978-1-4471-2353-8_4

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