An adaptive stochastic optimization method for medical registration problem

This paper presents a methodology that addresses important issues concerned with the optimization of the misregistration measures in the volumetric medical data registration problem. Our registration framework uses robust simulated annealing method to handle multiple local minima of the cost function. Our efforts have been centred on obtaining a reliable, efficient and generally applicable method to solve such optimization problems. This has been accomplished through developing an adaptive cooling schedule for the simulated annealing method. The proposed method is very reliable for the estimation of the global minimum in the optimization of objective functions with highly differentiated search space landscapes. We present the detailed description of the method as well as discussion of its advantages and disadvantages.