中文摘要：

基于互信息的配准方法具有精度高、鲁棒性强等特点,但互信息的配准函数存在局部极值,给配准的过程带来了很大的困难。针对此问题提出了以归一化互信息作为相似性测度,将具有较强全局搜索能力的量子粒子群优化（QPSO）算法用于求解低精度的配准参数,再利用具有较强局部搜索能力的Powell法获得高精度配准参数的方法,应用到医学图像的配准中。实验结果表明,提出的混合算法能够有效地克服互信息函数存在的局部极值和Powell方法存在的初始点依赖问题,提高了配准的成功率,具有较高的配准精度和较快的速度。

英文摘要：

The registration method based on mutual information has advantages of high precision,excellent robustness,etc.But there are many local minimums in the registration function of mutual information,which put difficulty on image registration.In view of this problem,the paper proposes the normalized mutual information as similar estimation.In the process of image registration,a low precision solution is solved by Quantum Particle Swarm Optimization（QPSO） algorithm firstly and then a high precision solution is acquired by Powell method.The QPSO algorithm has strong global search capability and Powell method has strong local search capability.Experimental results show that the proposed hybrid method can overcome the problem of the local minimums in the mutual information function and initial point dependence problem in Powell method.The hybrid method has improved the success rate of image registration,and has high registration precision and high speed.