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Identification of Translational Displacements between N-dimensional Data Sets Using the High-order SVD and Phase Correlation
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Institution: |
1Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. shoge&bwh.harvard.edu 2Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA |
Publisher: |
IEEE Trans Image Process |
Publication Date: |
Jul-2005 |
Volume Number: |
14 |
Issue Number: |
7 |
Pages: |
884-889 |
Citation: |
IEEE Trans Image Process. 2005 Jul;14(7):884-9. |
PubMed ID: |
16028552 |
Keywords: |
high-order SVD, phase correlation |
Appears in Collections: |
LMI, MIPG |
Sponsors: |
T32-EB002177 (EB) funded by NIBIB |
Generated Citation: |
Hoge W.S., Westin C-F. Identification of Translational Displacements between N-dimensional Data Sets Using the High-order SVD and Phase Correlation. IEEE Trans Image Process. 2005 Jul;14(7):884-9. PMID: 16028552. |
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| Paper: | Download, View online |
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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.
Additional Material
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Hoge-TIP2005-fig1.jpg (352.101kB)