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

Authors:
W. S. Hoge;C. -F. Westin
Affiliations:
Dept. of Radiol., Brigham & Women's Hosp., Boston, MA, USA;-
Venue:
IEEE Transactions on Image Processing
Year:
2005

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Abstract

This paper 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 three-dimensional MRI data sets, we demonstrate the effectiveness of this approach relative to other N-dimensional image registration methods.