Iterative multi-scale registration without landmarks

continuous set of non-singular 3-D affine transforms, which are the most general linear transformations available, and where by continuity we imply sub-pixel accuracy; our dissimilarity measure is Euclidean, which is maximum-likelihood assuming additive white Gaussian noise; finally, our search strategy is multi-scale, for fast convergence, and iterative, based on a variation of the MarquardtLevenberg (ML) algorithm for non-linear least-square optimizations [18]. 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 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. 2. SYSTEM