Identification of translational displacements between N-dimensional data sets using the high order SVD and phase correlation

W. S. Hoge, C.-F. Westin
IEEE Transactions on Image Processing
Volume 14, Number 7, Pages 884-889
July, 2005

Download full paper            DOI: 10.1109/TIP.2005.849327            PubMed: 16028552

Abstract

This manuscript presents an extension of the phase correlation image alignment method to N-dimensional data sets. By the Fourier shift theorem, the motion model for translational shifts between N-dimensional images can be represented as a rank-one tensor. Through use of a high-order singular value decomposition, the phase correlation between two N-dimensional data sets can be decomposed to independently identify translational displacements along each dimension with subpixel resolution. Using 3D MRI data sets, we demonstrate the effectiveness of this approach relative to other N-dimensional image registration methods.

Original data to be registered: three 3D fast-spin-echo MR acquisitions each with a unique high-resolution plane.

Reference

Hoge WS, Westin CF. Identification of translational displacements between N-dimensional data sets using the high order SVD and phase correlation. IEEE Transactions on Image Processing 2005;14(7):884-889.

Bibtex entry

@Article{hogeTIP2005,
  author         = {Hoge, W. Scott and Westin, Carl-Fredrik},                  
  title          = {Identification of translational displacements between      
                   {N}-dimensional data sets using the high order {SVD}  and   
                   phase correlation},                                         
  journal        = {IEEE Transactions on Image Processing},                    
  year           = {2005},                                                     
  volume         = {14},                                                       
  number         = {7},                                                        
  pages          = {884-889},                                                  
  month          = {July},                                                     
  pmid           = 16028552,                                                   
  doi            = {10.1109/TIP.2005.849327}
}

Grants

NIH T32-EB002177, NIH P41-RR13218 (NAC)

Research area

Registration

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