A cooperative search algorithm for mutual information based image registration

Mutual information (MI) has been used widely as a similarity measure for many multi-modality image registration problems. MI of two registered images is assumed to attain its global maximum. One major problem while implementing this technique is the lack of an efficient yet robust global optimizer. The direct use of existing global optimizers such as simulated annealing (SA) or genetic algorithms (GA) may not be feasible in practice since they suffer from the following problems: 1) When should the algorithm be terminated. 2) The maximum found may be a local maximum. The problems mentioned above can be avoided if the maximum found can be identified as the global maximum by means of a test. In this paper, we propose a global maximum testing algorithm for the MI based registration function. Based on this test, a cooperative search algorithm is proposed to increase the capture range of any local optimizer. Here we define the capture range as the collection of points in the parameter space starting from which a specified local optimizer can be used to reach the global optimum successfully. When used in conjunction with these two algorithms, a global optimizer like GA can be adopted to yield an efficient and robust image registration procedure. Our experiments demonstrate the successful application of our procedure.