针对影像导航手术提出了一种基于灰度距离融合的2D/3D刚性图像配准方法。该方法使用一种新的灰度距离信息对最常使用的传统相似性测度(互信息,互相关及模式强度)进行约束,构建一类新的相似性测度(距离互信息,距离互相关和距离模式强度),并用维也纳医科大学公开发表的2D/3D刚性配准金标准数据来评估新测度的配准性能。与基于灰度的传统相似性测度相比,文中方法构造的新相似性测度在平均目标配准误差(mTRE)的均值和标准差上均有显著降低,其中均值至少降低了28.15%,标准差最少降低了61.17%。以mTRE小于2 mm为配准成功的依据时,新测度的配准成功率比传统测度至少提高了25.56%。此外,新测度在配准优化过程中的迭代次数比传统测度平均降低了35.59%。结果显示：基于灰度距离融合的2D/3D刚性配准方法比基于单一灰度的配准方法具有更好的图像配准性能。

For image-guided surgery, a novel 2D/3D image rigid registration method is proposed by integrating intensity distances of images. The method uses a new intensity distance information to restrict the most commonly used similarity measures(Mutual Information (MI), Cross Correlation (CC) and Pattern Intensity (PI)) and to construct a kind of novel similarity measures(distance MI, distance CC and distance PI). These novel measures are evaluated by using the porcine skull phantom datasets from the Medical University of Vienna. The experiments show that novel measures are better than traditional measures,i.e., the mean and standard deviation of mean Target Registration Errors (mTRE) by novel measures are respectively lower by at least 28.15% and 61.17% than those by traditional measures. When setting mTRE less than 2 mm as successful registration, the success rate with novel measures increases by at least 25.56% on average. Meanwhile, the average iteration times of novel measures also reduce by 35.59% than those of traditional measures. This results suggest that the novel registration method using novel measures has better performance of registration than intensity-based methods using traditional measures in terms of the accuracy and robustness for 2D/3D rigid registration.