A hierarchical approach to elastic registration based on mutual information

A hierarchical approach to elastic registration based on mutual information, in which the images are progressively subdivided, locally registered, and elastically interpolated, is presented. To improve the registration, a combination of prior and floating information on the joint probability is proposed. It is shown that such a combination increases the registration speed at the coarser levels in hierarchy, enables a registration of finer details, and provides additional guidance to the optimisation process. Besides, a threefold local registration consistency test and correction of shading were employed to increase the overall registration performance. The proposed hierarchical method for elastic registration was tested on an experimental database of 2D images of histochemically differently stained serial cross-sections of human skeletal muscle. The obtained results show that 95% of the images could be successfully registered. The inclusion of prior information is an important break through that may enable routine use of the mutual information cost function in a variety of 2D and 3D image registration algorithms in the future.