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.