| In clinical diagnosis and treatment, it is usual to have more images of focus of patient. In order to obtain the complementary and effective and comprehensive information, it is necessary for more medical images to fuse information. As the basis of images information fusing, medical images registration have important applied values, it is a hot point in the medical images treatment.Medical images registration want to look for the best transformation parameters, and match corresponding points of images, and express many-sided information in one image. Medical images have complexity and difficulty. Although many methods have been brought forward, they have some limitation because every registration method is only designed for the given problem. Moreover, registration precision and speed can not obtain perfect effect at the same time. The paper bring into the newest registration model based on gray statistics: mutual information registration model, and the method which based on it is called the maximal mutual information method. The method takes mutual information in the information theory as similarity between two registration images, and it does not need processing ahead of time such as segment, and it almost suits to any kinds of image registration. The paper uses genetic algorithm which has good global searching ability. And improves the standard genetic algorithm from coding,genetic operating ,and so on. It improves the capability of the algorithm.The paper designs a registration algorithm which links improved genetic algorithm and the maximal mutual information method for two 2D brain images. It takes mutual information model and bases on the intensities of the images, and uses improved genetic algorithm to search the best transformation parameters. It also uses the maximal mutual information as the aim function to guide searching the best transformation parameters, and the effectiveness of the algorithm is proved. |
