Robust Multimodal Retinal Image Registration in Diabetic Retinopathy Using a Light-Weight Neural Network and Improved RANSAC Algorithm

Diabetic retinopathy (DR) poses a significant risk of vision loss due to diabetes-related damage to retinal blood vessels. Early detection and analysis of DR progression are crucial for effective management. Retinal images offer a non-invasive means for monitoring DR, requiring precise registration over time and across imaging modalities. To address this, we introduce MedRegNet, an extended light-weight multimodal image registration framework. Leveraging deep learning, our method employs a feature-based registration approach using interest point detection and matching, enhanced using novel transformation recovery techniques by adjusting Random Sample Consensus (RANSAC). By prioritizing coverage of matched points over the entire image, our method significantly boosts registration robustness. Additionally, coupling MedRegNet with a supervised fovea detection U-Net enhances plausibility checking before matching points, further increasing the number of good matches and robustness. Evaluation on challenging multimodal DR datasets demonstrates MedRegNet’s capability to precisely register color fundus and fluorescein angiography images. Our method achieves registration accuracy ranging from 83% to 97% of the maximum possible with homographies, successfully registering 264 out of 266 image pairs across validation and test datasets. MedRegNet offers superior registration results compared to previous approaches, particularly on multimodal DR data, showcasing its efficacy in facilitating accurate and reliable DR progression analysis.