Abstract
Retinal imaging is crucial in diagnosing and treating retinal diseases, and multimodal retinal image registration constitutes a major advance in understanding retinal diseases. Despite the fact that many methods have been proposed for the registration task, the evaluation metrics for successful registration have not been thoroughly studied. In this article, we present a comprehensive overview of the existing evaluation metrics for multimodal retinal image registration, and compare the similarity between the subjective grade of ophthalmologists and various objective metrics. The Pearson's correlation coefficient and the corresponding confidence interval are used to evaluate metrics similarity. It is found that the binary and soft Dice coefficient on the segmented vessel can achieve the highest correlation with the subjective grades compared to other keypoint-supervised or unsupervised metrics. The paper established an objective metric that is highly correlated with the subjective evaluation of the ophthalmologists, which has never been studied before. The experimental results would build a connection between ophthalmology and image processing literature, and the findings may provide a good insight for researchers who investigate retinal image registration, retinal image segmentation and image domain transformation.
Highlights
R ETINAL diseases, including age-related macular degeneration, diabetes retinopathy, and vascular occlusion, are leading causes of multiple retina pathologies, and have systemic implications
We propose a method to mathematically compare the similarity of the subjective grade and the commonly used objective evaluation metrics, and establish an objective evaluation metric that is most correlated with the subjective evaluation of the ophthalmologists
In order to build a connection between ophthalmology and image processing literature, in this paper, we compute the Pearson correlation coefficient [25] between the subjective grade of ophthalmologists and various objective evaluation methods to compare their degree of similarity
Summary
R ETINAL diseases, including age-related macular degeneration, diabetes retinopathy, and vascular occlusion, are leading causes of multiple retina pathologies, and have systemic implications. For retinal image registration tasks, RMSE less 5 pixels is usually considered as success registration [11], [12], [17], [18], and the threshold T can be set to 5 pixel To compute these metrics, we need to first manually label pairs of keypoint correspondences (generally 6 or more [10]–[12], [17], [18]) for all the multimodal images, where the keypoint locations should accurately lie on salient landmarks like vessel bifurcations, and uniformly distributed in the overlapping area. We need to first manually label pairs of keypoint correspondences (generally 6 or more [10]–[12], [17], [18]) for all the multimodal images, where the keypoint locations should accurately lie on salient landmarks like vessel bifurcations, and uniformly distributed in the overlapping area METHOD In order to build a connection between ophthalmology and image processing literature, in this paper, we compute the Pearson correlation coefficient [25] between the subjective grade of ophthalmologists and various objective evaluation methods to compare their degree of similarity
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