中文摘要：

为提高医学图像辅助诊断的配准精度和收敛速度,提出了一种基于混合互信息和改进粒子群优化算法的医学图像配准算法,在每步迭代中,先用基于Renyi熵的互信息结合改进粒子群优化算法对图像进行全局搜索,然后对当前得到的最优解使用基于Shannon熵的Powell算法进行局部寻优。实验结果表明,该算法在收敛速度和精度方面都优越于其他配准算法。

英文摘要：

A novel medical image registration method based on mixed mutual information and improved particleswarm optimization algorithm was proposed to meet the needs of medical image diagnosis and treatment. During eachiteration of the proposed algorithm, the improved particle swarm optimization algorithm based on Renyi＇s entropywas adopted firstly in global searching phase. Then the mutual information measure based on Shannon＇s entropy wastaken as the objective function while the Powell algorithm was used to obtain the optimal solution in local searchingphase. Comparing with the other three algorithms in the experiment of multi-modality medical image registration, theresults showed that the proposed algorithm has the advantage in accuracy and effectiveness over other image registra-tion methods.