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Tensor Field Regularization Using Normalized Convolution and Markov Random Fields in a Bayesian Framework
Harvard Medical School. (Laboratory of Mathematics in Imaging (LMI))
University ov Valladolid.
University ov Valladolid.
University of Las Palmas de Gran Canaria.
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2006 (English)In: Visualization and Image Processing of Tensor Fields / [ed] Joachim Weickert, Hans Hagen, Springer Berlin/Heidelberg, 2006, 381-398 p.Chapter in book (Refereed)
Abstract [en]

This chapter presents two techniques for regularization of tensor fields. We first present a nonlinear filtering technique based on normalized convolution, a general method for filtering missing and uncertain data. We describe how the signal certainty function can be constructed to depend on locally derived certainty information and further combined with a spatially dependent certainty field. This results in reduced mixing between regions of different signal characteristics, and increased robustness to outliers, compared to the standard approach of normalized convolution using only a spatial certainty field. We contrast this deterministic approach with a stochastic technique based on a multivariate Gaussian signal model in a Bayesian framework. This method uses a Markov random field approach with a 3D neighborhood system for modeling spatial interactions between the tensors locally. Experiments both on synthetic and real data are presented. The driving tensor application for this work throughout the chapter is the filtering of diffusion tensor MRI data.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2006. 381-398 p.
, Mathematics and Visualization, ISSN 1612-3786
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-38770DOI: 10.1007/3-540-31272-2_24ISI: 000235580100024Local ID: 45553ISBN: 3-540-25032-8ISBN: 978-3-540-31272-7OAI: diva2:259619
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2015-10-09Bibliographically approved

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