Tensor field regularization using normalized convolution

C.-F. Westin, H. Knutsson
Computer Aided Systems Theory (EUROCAST'03), LNCS 2809
Pages 564-572
February 24-28, 2003

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Abstract

This paper presents a filtering technique for regularizing tensor fields. We use a nonlinear filtering technique termed normalized convolution [Knutsson andWestin 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.

Result of filtering the DT-MRI tensor field using the proposed method.

Reference

Westin CF, Knutsson H. Tensor field regularization using normalized convolution. In RM Diaz, AQ Arencibia, eds., Computer Aided Systems Theory (EUROCAST'03), LNCS 2809. Las Palmas de Gran Canaria, Spain: Springer Verlag, 2003;564-572.

Bibtex entry

@INPROCEEDINGS{westinEUROCAST03,
  author         = {Carl-Fredrik Westin and Hans Knutsson},                    
  title          = {Tensor field regularization using normalized convolution}, 
  editor         = {Roberto Moreno Diaz and Alexis Quesada Arencibia},         
  booktitle      = {Computer Aided Systems Theory (EUROCAST'03), LNCS 2809},   
  pages          = {564--572},                                                 
  month          = {February 24--28},                                          
  year           = {2003},                                                     
  address        = {Las Palmas de Gran Canaria, Spain},                        
  publisher      = {Springer Verlag},                                          
  note           = {}
}

Grants

NIH P41-RR13218 (NAC), CIMIT, NIH R01-MH50747

Research areas

DTMRI, Tensor

Copyright Information

Spinger-Verlag