Towards efficient registration of medical images.

In this paper we propose a Bayesian based mutual information technique for image registration, combined with an established affine transformation model. Classical affine models allow the images to be approximately aligned. However, inefficiency and inaccuracy has appeared when using these affine models in rigorous circumstances, such as low-resolution images. To challenge this problem, we conduct mutual information measures with importance sampling to the images in an attempt to simulate the probability distribution of intensity similarity across the images. The entire registration adopts a stopping criterion as discovered in the context of differential equations. Finally, experimental results demonstrate the favorable performance of the proposed algorithm.