Registration of Brain Medical Images Based on SURF Algorithm and R-RANSAC Algorithm

This paper proposes a matching method for medical image registration, which combined with SURF (Speeded up Robust Features) algorithm and the improved R-RANSAC (the Randomized of Random Sample Consensus) algorithm. Firstly, this algorithm extracts featured points with SURF algorithm from images and matches similar featured points with Euclidean distance. Secondly, the RRANSAC algorithm is used to eliminate wrong matches and the SPRT (Sequential Probability Ratios Test) is used to minimize R-RANSAC runtime. Finally, the image registration process is accomplished by estimating space geometric varied parameters according to least square method. The algorithm combines robustness and high efficiency of SURF and high-accuracy of R-RANSAC algorithm. Experimental results show that in the condition of images with noise, non-uniform intensity and large scope of the initial misalignment, the proposed algorithm achieves better robustness and higher speed while maintaining good registration accuracy compared with the conventional area-based and feature-based registration methods.