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中文摘要：
为提高医学图像配准效果，提出了一种基于小波变换和互信息的配准方法。该方法首先通过小波变换将图像分层，并用小波分解的近似分量从最顶层开始搜索，同时以添加边界约束条件的下降单纯形法为搜索策略，而以搜索结果作为下一层搜索的粗略位置；然后逐层细化，以实现由粗到细的搜索过程；同时，针对不同的分解层采用不同的配准方法，即下层引入结合空间信息的区域互信息（RMI）为相似性测度，而上层采用PV插值法，以避免陷入局部极值。最后将此法应用于加噪MR图像单模配准、PET图像单模配准和MR-PET图像多模配准的。实验结果表明，该方法可以得到精确、有效的配准结果。与传统方法相比，该方法不仅配准精度高、抗噪性能好，而且计算效率高。
英文摘要：
To improve the performance of medical image registration technology, a new method based on wavelet transformation and mutual information is proposed in this paper. Decomposed wavelet sub-bands of the original images are obtained using the wavelet transform. Coarse-to-fine multi-resolution search approaches have been performed. Registration at higher levels can be performed with the result at the pervious level serving as the initial condition. Down simplex method with limited boundary is used as optimization strategy. Besides, different methods are used at different levels. Regional mutual information （RMI） which takes geometry into account is used as similarity measure at low levels and PV interpolation is applied at high level in order to prevent the optimizing process from being tapped into local maximums. The algorithm has been applied on noisy MR mono-modality, PET mono-modality and MR-PET muhimodality medical image registration. The results show that the algorithm performs fairly well. Compared with the traditional algorithms, the algorithm has some advantages such as higher precision and better anti-nolsy performance as well as higher computation efficiency.
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