Registration of retinal image is a crucial and fundamental step in several medical diagnoses. In this paper we propose an innovative method for retinal image registration. The method applies log-polar transform to approximate the difference in scale and orientation among images. A novel descriptor named Combined Local Haar of Bifurcation points (CLHB) is proposed for robust description and precise matching of retinal bifurcation and cross-over points. Experiments are performed on retinal image registration datasets collected from private and public sources and consisting of a total of 484 fundus photographs (i.e. 242 pairs). The proposed method has been compared with the state-of-the-art Generalized Dual-Bootstrap Iterative Closest Point (GDP ICP), Hernandez-Matas et al., Saha et al., and Chen et al.’s methods and has been found to outperform them with a clear margin. On the publicly available FIRE dataset, our proposed method is found 2% more accurate than the best performing Saha et al.’s method. On the private dataset the method is found to be about 3% more accurate than the best performing method.