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Two-stage color fundus image registration via Keypoint Refinement and Confidence-Guided Estimation
Feihong Yan1, Yubin Xu1, Yiran Kong1
1Beijing Institute of Technology, No. 5, Zhong Guan Cun South Street, Beijing, 100081, China.
Summary
This study introduces a new two-stage method for registering color fundus images, crucial for tracking eye disease progression. The approach enhances accuracy without needing training data, improving diagnostic capabilities.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Color fundus images are vital for diagnosing eye conditions like glaucoma and diabetic retinopathy.
- Accurate image registration is essential for monitoring disease progression by detecting subtle changes.
Purpose of the Study:
- To propose a novel, end-to-end framework for color fundus image registration.
- To achieve accurate registration without requiring training or annotated data.
Main Methods:
- A two-stage registration framework utilizing pre-trained SuperPoint and SuperGlue networks.
- Refinement of initial matching pairs based on slope analysis.
- Confidence-Guided Transformation Matrix Estimation (CGTME) using a novel CG 4-point algorithm.
Main Results:
- The proposed CGTME method effectively refines perspective transformation matrix estimation.
- High-confidence matched points are selected for improved registration accuracy.
- Experimental results demonstrate significant improvements in registration performance.
Conclusions:
- The novel two-stage framework offers an effective solution for color fundus image registration.
- The method enhances the assessment of disease progression by improving registration accuracy.
- This approach provides a valuable tool for ophthalmological diagnostics without reliance on training data.
