A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method

Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image’s limited field of view might be insufficient. By combining multiple images...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-09, Vol.23 (18), p.7809
Main Authors: Ochoa-Astorga, Jesús Eduardo, Wang, Linni, Du, Weiwei, Peng, Yahui
Format: Article
Language:English
Subjects:
Accuracy
blood vessel bifurcation
Blood vessels
Datasets
Deep learning
Diabetic retinopathy
Diagnosis
fundus image
image registration
Longitudinal studies
Medical research
Medicine, Experimental
Methods
Neural networks
Photography
Registration
registration error
Retina
retinal analysis
Sensors
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Summary:Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image’s limited field of view might be insufficient. By combining multiple images, the field of view for retinal analysis is extended, and resolution is enhanced through super-resolution imaging. Moreover, this method facilitates patient follow-up through longitudinal studies. This paper proposes a straightforward method for fundus image registration based on bifurcations, which serve as prominent landmarks. The approach aims to establish a baseline for fundus image registration using these landmarks as feature points, addressing the current challenge of validation in this field. The proposed approach involves the use of a robust vascular tree segmentation method to detect feature points within a specified range. The method involves coarse vessel segmentation to analyze patterns in the skeleton of the segmentation foreground, followed by feature description based on the generation of a histogram of oriented gradients and determination of image relation through a transformation matrix. Image blending produces a seamless registered image. Evaluation on the FIRE dataset using registration error as the key parameter for accuracy demonstrates the method’s effectiveness. The results show the superior performance of the proposed method compared to other techniques using vessel-based feature extraction or partially based on SURF, achieving an area under the curve of 0.526 for the entire FIRE dataset.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23187809