A multimodal image registration method combining gradient orientation mutual information with multi-resolution hybrid optimization algorithm

In order to solve the problem that the image registration method based on the classical mutual information may suffer from local maxima and these improved methods combing with gradient can not align the multimodal images because of the great difference on gradient amplitude, a novel multimodal image registration method is proposed. During the optimization of transform parameters, a hybrid optimization algorithm based on genetic algorithm (GA) and Powell is carried out to efficiently restrain local maxima of this new similarity measure function. The former provides the latter with effective initialization parameter, which will increase the algorithm's robustness. Multi-resolution data structure based on wavelet transform is used to expedite the registration process. Experimental results demonstrate that this new algorithm can efficiently speed up the registration process with a good registration result.