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Tensor field regularization using normalized convolution
Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA .
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2003 (English)In: Computer Aided Systems Theory - EUROCAST 2003: 9th International Workshop on Computer Aided Systems Theory Las Palmas de Gran Canaria, Spain, February 24-28, 2003 Revised Selected Papers / [ed] Roberto Moreno-Díaz and Franz Pichler, Springer Berlin/Heidelberg, 2003, Vol. 2809, 564-572 p.Chapter in book (Refereed)
Abstract [en]

This paper presents a filtering technique for regularizing tensor fields. We use a nonlinear filtering technique termed normalized convolution [Knutsson and Westin 1993], a general method for filtering missing and uncertain data. In the present work we extend the signal certainty function to depend on locally derived certainty information in addition to the a priory voxel certainty. This results in reduced blurring between regions of different signal characteristics, and increased robustness to outliers. A driving application for this work has been filtering of data from Diffusion Tensor MRI.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2003. Vol. 2809, 564-572 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 2809
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-48437DOI: 10.1007/978-3-540-45210-2_51ISBN: 978-3-540-20221-9ISBN: e-978-3-540-45210-2ISBN: 3-540-20221-8OAI: diva2:269333
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-11-21Bibliographically approved

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