基于经验模式分解和互信息的多模态图像配准

基于互信息的配准方法是目前多模态图像配准研究中的热点.提出了一种基于经验模式分解后剩余图像和互信息的多模态图像配准方法.首先通过理论分析得出通过求解剩余图像之间的变换参数即可获得原始图像之间的变换参数,从而论证了二维经验模式分解(BEMD)应用于多模态图像配准的可行性,然后给出了图像配准方法的实现步骤.典型多模态图像配准实验结果表明此方法与传统互信息法和基于小波分解结合互信息的方法相比,旋转角度估计误差可以降低1个数量级,缩放参数的估计误差也有很大降低.表明该方法获得了更高的配准精度.

Multi-modal image registration based on empirical mode decomposition and mutual information

Image registration based on mutual information has received much attention in multi-modality image registration at present. A method of multi-modality image registration based on remnant image and mutual information was proposed. Firstly, through theoretic analysis it is found that the transformation parameters between original images could be acquired by solving the transformation parameters between remnant images, which demonstrates the feasibility that bidimensional empirical mode decomposition (BEMD) could be successfully applied to multi-modality image registration. Then the implementation steps of the proposed method are given. Experiment results of typical multi-modality images demonstrate that the estimation error of rotation transformation parameter for this method can be reduced by one order in magnitude compared with traditional mutual information and wavelet composition method, which indicates that this method can obtain higher accuracy and better registration effect.