Image registration using consistent robust point matching

The robust point matching (RPM) has been applied in image registration. Although the RPM has been extensively used in non-rigid registration of images, it does not solve a consistent correspondence between the two images except at the estimated corresponding points. In this paper, we propose an image registration method based on the consistent robust point matching to solve the forward and reverse transformations between two images. It defines a novel cost function for RPM by introducing the inverse consistency constraint. The fuzzy corresponding relationship between points is estimated based on both the forward and reverse transformations simultaneously. The proposed method also introduces similarity of image content in the procedure of points matching. The performance of our method is demonstrated and validated in synthetic experiments. Moreover, we apply the method to the problem of non-rigid registration of real images and brain images. The results indicate the improvement of the proposed method in the aspect of bi-directional image registration and the decrease of the inverse consistency errors of the forward and the reverse transformations between two images.