Logarithmic Fuzzy Entropy Function for Similarity Measurement in Multimodal Medical Images Registration Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To integrate multimodal information, multimodal registration is significant. The entropybased registration applies a structure descriptor set to replace the original multimodal image and compute similarity to express the correlation of images. The accuracy and converging rate of the registration depend on this set. We propose a new method, logarithmic fuzzy entropy function, to compute the descriptor set. It is obvious that the proposed method can increase the upper bound value from log(r) to log(r)xe2x80x89xe2x80x89xe2x88x86(r) so that a more representative structural descriptor set is formed. The experiment results show that our method has faster converging rate and wider quantified range in multimodal medical images registration.

MultiModal Medical Volume Registration Using a New Information Theoretic Measure This work presented a new 3D registration approach for multimodal medical volumes. Our approach employed a generalized entropy called Arimoto entropy, which is a generalization of Shannon entropy. Our method proposed in this article applies the Jensen Arimoto divergence as a registration criterion and measures the similarity between the 3D medical volumes acquired from various modalities using this new criterion. The goal of this work is to obtain the maximum value of the new registration criteria by exploiting the quasiNewton optimization scheme. Simultaneously, two volumes are completely registered, along with the final spatial transformation obtained. In order to evaluate our presented algorithm, the experiments on real threedimensional medical volumes are designed and performed. Results of registration experiments illustrated that our approach is more effective and proves better registration accuracy. Additionally, a comparison with two classic measures based on information theory, crosscumulative residual entropy and normalized mutual information, is carried out.