Study on Correlation Between Subjective and Objective Metrics for Multimodal Retinal Image Registration

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 regi...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.190897-190905
Main Authors: Wang, Yiqian, Zhang, Junkang, Cavichini, Melina, Bartsch, Dirk-Uwe G., Freeman, William R., Nguyen, Truong Q., An, Cheolhong
Format: Article
Language:English
Subjects:
Confidence intervals
Correlation
Correlation analysis
Correlation coefficients
dice coefficient
Diseases
Image processing
Image registration
Image segmentation
image similarity
Imaging
Measurement
Medical personnel
Multimodal retinal imaging
objective metric
Ophthalmology
Pearson correlation
Registration
Retina
Retinal images
Similarity
subjective metric
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3032348