Multi-resolution rigid image registration using bacterial multiple colony chemotaxis

A novel approach for image registration based on mutual information is presented in this paper. Firstly, normalized mutual information is introduced as the fitness function to supersede the traditional mutual information. Meanwhile, the extrema in fitness function resulted from interpolation is analyzed and the solution is presented as adding a weighted parameter into the variables set. Secondly, search model is improved. The 2^nd generation wavelet is utilized to decompose the original image into multi-resolution sub-images to search optimal solution in different precision layer, which harmonizes the contradiction between search precision and convergence rate. Finally, we introduce a novel optimization means—bacterial multiple colony chemotaxis. Experiments demonstrate the legitimacy and efficiency of our betterment.