Sequential Anisotropic Wiener Filtering Applied to 3D MRI Data

M. Martin-Fernandez, C. Alberola-Lopez, J. Ruiz-Alzola, C.-F. Westin
Magnetic Resonance Imaging
Volume 25, Pages 278-292
2007

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Abstract

We present three different sequential Wiener filters, namely, isotropic, orientation and anisotropic. The first one is similar to the classical Wiener filter in the sense that it uses an isotropic neighborhood to estimate its parameters. Here we present a sequential version of it. The orientation Wiener filter uses oriented neighborhoods to estimate the structure orientation present at each voxel, giving rise to a modified estimator of the parameters. Finally, the anisotropic Wiener filter combines both approaches adaptively so that the appropriate approach is locally selected. Several synthetic experiments are presented showing the performance of the filters with respect to their parameters. A mean square error analysis is performed using a publicly available magnetic resonance imaging (MRI) brain phantom and a comparison with other filtering approaches is carried out. In addition, results from filtering real MRI data are presented.

Sequential Wiener filtering of MRI data: (A) original image, (B) with added noise, (C) sequential anisotropic Wiener solution and (D) boundary mask.

Reference

Martin-Fernandez M, Alberola-Lopez C, Ruiz-Alzola J, Westin CF. Sequential anisotropic wiener filtering applied to 3D MRI data. Magnetic Resonance Imaging 2007;25:278-292.

Bibtex entry

@Article{martin-fernandezMRI07,
  author         = {M. Martin-Fernandez and C. Alberola-Lopez and J.           
                   Ruiz-Alzola and C.-F. Westin},                              
  title          = {Sequential Anisotropic Wiener Filtering Applied to {3D}    
                   {MRI} Data},                                                
  journal        = {Magnetic Resonance Imaging},                               
  year           = {2007},                                                     
  volume         = {25},                                                       
  pages          = {278--292}, }

Grants

NIH P41-RR13218 (NAC), Fulbright FU2003-0968

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