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REMPE: Registration of Retinal Images Through Eye Modelling and Pose Estimation
IEEE Journal of Biomedical and Health Informatics
|April 6, 2020
Summary
Accurate retinal image registration is crucial for monitoring eye diseases. This new method uses eye modeling for improved accuracy in fundus images, outperforming existing techniques and offering a publicly available solution.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- In-vivo assessment of small retinal vessels aids in diagnosing and monitoring vasculopathy, like hypertension and diabetes.
- The retina offers a unique window for non-invasive imaging of small vessels via fundoscopy.
- Accurate registration of retinal images is vital for comparing vessel measurements over time, essential for disease management.
Purpose of the Study:
- To enhance the accuracy of retinal image registration for improved disease monitoring.
- To address challenges in retinal registration caused by the retina's curvature and tissue changes.
Main Methods:
- A novel registration framework is proposed that simultaneously estimates eye pose and shape.
- The method utilizes corresponding points in retinal images to perform registration as a 3D pose estimation problem.
Main Results:
- The proposed framework demonstrates superior performance compared to state-of-the-art methods in retinal image registration.
- Quantitative evaluations on benchmark datasets confirm the method's improved accuracy for fundoscopy images.
Conclusions:
- Eye modeling-based retinal image registration methods yield higher accuracy than conventional approaches.
- This study presents the first method combining retinal image registration with eye modeling, advancing the field.
