Identification of translational displacements between N-dimensional data sets using the high-order SVD and phase correlation.
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.
Hoge
W Scott
WS
Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. shoge@bwh.harvard.edu
Westin
Carl-Fredrik
CF
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology; D016000:Cluster Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique