Multiple Medical Image Registration Using Entropy of Arithmetic Geometric Mean Divergence Matrix

Mutual information has been proved an efficient measure for medical image registration. However it is confined in aligning two images and hard to be applied to mapping multiple images because of its large computational cost. A new measure for multiple medical image registration is proposed based on the theory of high dimensional mutual information and arithmetic geometric mean (AGM) divergence. The method first calculates the high dimensional arithmetic geometric mean matrix, and then calculates the entropy of the matrix. The maximal entropy corresponds to the optimal registration solution. The method is tested on brain images. The obtained results show that the proposed method can dramatically decrease registration time, which is a very important consideration in clinical use, with acceptable accuracy.