Feature-Based Retinal Image Registration by Enforcing Transformation-Guided and Robust Estimation

Retinal image registration plays an important role to diagnose, monitor and track the progress of various related fundus disease. Since the vascular structure is indistinct in poor quality and low overlap retinal images, it becomes more difficult for the intensity-based and blood vessel, branch and cross points-based registration methods. In order to solve this issue, an effective retinal image registration method is proposed by enforcing transformation-guided and robust estimate in this paper. The landmark is extracted to build the initial correspondence set. And the correspondences are refined by using affine model and quadric model to guide estimator for removing the mismatches. The robust regression estimate method (iteratively reweighted least squares) combined the M-estimator is used to calculate the transformation parameter for affine model and quadric model hierarchically for warping the moving images. We evaluate the proposed framework by quantitative measurements and visual comparison and the results demonstrate that the proposed framework is more robust for estimating the transformation parameter and obtain more accurate re registration results than other methods.