为了解决灰度图像配准中由于目标函数容易陷入局部极值而造成的误匹配问题, 使参数随图像的NMI计算和多分辨率级数进行自适应调整, 采用基于小波变换多分辨率策略,形成多尺度匹配模型, 并将粒子群算法(PSO)作为添加算子, 提出了以图像归一化互信息(NMI)作为相似性测度的混合遗传算法, 对CT与MRI图像进行了配准。实验结果表明,该方法能够解决遗传算法早熟收敛问题, 有效地克服信息函数的局部极值, 实现图像的自动配准, 具有匹配精确、鲁棒性好及效率高等优点。

To address the disadvantages of image registration, a new method for image registration is proposed, which combines hybrid genetic algorithm with wavelet multi-resolution analysis strategy. In this method, mutual information is used as the similarity measure and a hybrid genetic algorithm is used as the search technique, and the parameters of genetic algorithm are adapted along with the computation of mutual information and the multi-resolution of the images. The experimental results show that this registration method could efficiently restrain the local maxima of mutual information function and the subvoxel accuracy can be achieved, which demonstrates that the algorithm is accurate, robust and efficient for image registration.