Adaptive search space scaling in digital image registration

Abstract

An image registration technique for application in X-ray, gamma-ray, and magnetic resonance imaging is described. The technique involves searching a real-valued, multidimensional, rectangular, symmetric space of bilinear geometrical transformations for a globally optimal transformation. Physical considerations provide theoretical limits on the search space, but the theoretically maximum allowable space is still often much larger than the smallest rectangular symmetric subspace that contains the optimal transformation. To reduce the search time, the current practice is to guess an optimal subspace from the maximum allowable space. This reduced space is then discretized and searched. An automatic technique to estimate adaptively a subspace from the maximum space during the search process itself is described. This adaptive technique is tested with two quite different types of search algorithms, namely, genetic algorithms and simulated annealing.