结合形态学梯度互信息和多分辨率寻优的图像配准新方法

A Novel Image Registration Method Combining Morphological Gradient Mutual Information with Multiresolution Optimizer

对互信息配准法进行算法改进.在互信息基础上结合形态学梯度作为新的图像配准测度,不仅考虑所有体素信息,而且有效结合像素在空间位置的相互关系.将粒子群优化(Particle swarm optimization,PSO)算法这种全局寻优算法和Powell这一局部寻优算法相结合,前者的配准结果为后者的算法优化提供了非常有效的初始点,优化时间大为减少.借鉴小波变换中多分辨率的思想,在低分辨率图像中粗略配准后,上升到高分辨率图像上进一步细化配准结果,增加算法鲁棒性.实验结果证明,本文算法效果良好,寻优过程在数分钟内完成,能够满足诊断和科研的实时性要求.

An improved image registration method is proposed based on mutual information.Firstly,a new registration measure function,combining mutual information with morphological gradient information of the images,is used to take the advantages of these two different indices to achieve the global optimization.Secondly,a hybrid optimizer based on particle swarm optimization（PSO）and Powell is applied to efficiently restrain local maximums of our new registration measure function.Lastly,multiresolution data structure based on wavelet transform is used to expedite the registration process and increase the algorithm＇s robustness.Experimental results demonstrate that this new algorithm can efficiently yield a good registration result and can achieve subvoxel accuracy,while meeting the real-time need of diagnostic and research purposes.