M. Unser, Nat. Inst. of Health, Bethesda, MD, USA
We present an automatic sub-pixel registration algorithm that minimizes the mean square difference of intensities between a reference and a test data set (volumes or images). It uses spline processing, is based on a coarse-to-fine pyramid strategy, and performs minimization according to a variation of the iterative Marquardt-Levenberg (1963) scheme. The geometric deformation model is a general affine transformation that one may optionally restrict to a rigid-body (isometric scale, rotation and translation), procrustean (rotation and translation) or translational case; it also includes an optional parameter for the linear adaptation of intensity. We present several PET and fMRI experiments and show that this algorithm provides excellent results. We conclude that the multi-resolution refinement strategy is faster and more robust than a comparable single-scale one.
Index Terms:
iterative methods; image registration; splines (mathematics); image resolution; medical image processing; positron emission tomography; biomedical NMR; iterative multiscale registration; automatic subpixel registration algorithm; mean square difference; test data set; volumes; images; spline processing; coarse-to-fine pyramid strategy; minimization; iterative Marquardt-Levenberg scheme; geometric deformation model; general affine transformation; isometric scale; rotation; translation; linear adaptation; multiresolution refinement; fMRI experiments; PET experiments
Citation:
P. Thevenaz, U.E. Ruttimann, M. Unser, "Iterative multi-scale registration without landmarks," icip, vol. 3, pp.3228, 1995 International Conference on Image Processing (ICIP'95) - Volume 3, 1995
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