Similarity measure based on minkowski generalized divergence for multimodal image registration

Based on the relationship between the joint probability distribution function of two images and the similarity between images, the connection between Shannon mutual information and Kullback-Leibler divergence is investigated. Thus a novel definition of divergence measure based on Minkowski inequality is proposed. On the proposed Minkowski generalized divergence the corresponding similarity measure for multimodal image registration is put forward. Unlike the information theoretic registration measures, Minkowski generalized divergence does not require that the condition of absolute continuity must be satisfied by the probability distributions involved. The new measure is applied to the rigid registration of clinical multimodal medical images. Experiment results show that the Minkowski similarity measure, when compared with information therotic measures, is more tolerable to noise and easier to implement in the Minkowski similarity measure function clue to its simplicity, e.g. in the use of power operation instead of logarithmic computation avoiding division.