Medical Image Registration Method Based on Mixed Mutual Information
基于混合互信息的医学图像配准

Traditionally, the similarity metric is based on Shannon's entropy. Through the analysis of Renyi's entropy, it is found that Renyi's entropy can remove some unwanted local optimum, smooth out difficult optimization terrain accordingly; Shannon's entropy has the "depth" of the basin of attraction, making the registration function easier to be optimized. So a new similarity measure based on mixed mutual information was proposed. The measures based on different entropy were used in different searching phases, and global optimization algorithm and local one were used individually. At first, the global optimization algorithm was used to find the local extrema of generalized mutual information measure based on Renyi's entropy. Then, the local one was used to locate the global optimal solution by searching the current local optimal ones, and the generalized mutual information measure based on Shannon's entropy was taken as the objective function.