A Pair-Metamorphosis-Decouple Synthetic Data Scheme for Color Fundus Image Registration

Color fundus (CF) image registration is crucial for accurate information fusion; it could obtain more details of retinal structure to assist clinical diagnosis. Existing methods suf-fer from costing time or dataset size, making CF image reg-istration still a challenging task. In this paper, we propose a novel pair-metamorphosis-decouple synthetic data scheme for learning-based CF image registration and ameliorate the registration model for retinal image. Specifically, we take ad-vantage of the pairing information of the registration task to decouple the differences between the pairing data and expand the representative ability of the dataset by synthesizing data. Furthermore, the registration framework is ameliorated ac-cording to the characteristics of the blood vessels in the retinal image. Experiments on the public dataset (FIRE) show that our synthetic data scheme could bring general performance promotion to registration models, and our registration method is superior to other state-of-the-art unsupervised algorithms.