FIRE: Fundus Image Registration dataset
نویسندگان
چکیده
Purpose: Retinal image registration is a useful tool for medical professionals. However, evaluating the accuracy of these registrationmethods has not been consistently undertaken in the literature. To address this, a dataset comprised of retinal image pairs annotated with ground truth and an evaluation protocol for registration methods is proposed. Methods: The dataset is comprised of 134 retinal fundus image pairs. These pairs are classified into three categories, according to characteristics that are relevant to indicative registration applications. Such characteristics are the degree of overlap between images and the presence/absence of anatomical di erences. Ground truth in the form of corresponding image points and a protocol to evaluate registration accuracy are provided. Results: Using the aforementioned protocol, it is shown that the Fundus Image Registration (FIRE) dataset enables quantitative and comparative evaluation of retinal registration methods under a variety of conditions. Conclusion: This work enables the fair comparison of retinal registration methods. It also helps researchers to select the registration method that is most appropriate given a specific target use.
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