Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images

TL;DR

The paper introduces Particle Diffusion Matching (PDM), a novel robust alignment method for Standard and Ultra-Widefield Fundus Images using a diffusion-guided random walk approach, improving accuracy in challenging conditions.

Contribution

It presents a new diffusion-guided iterative correspondence search technique that enhances retinal image alignment across different modalities and scales.

Findings

Achieves state-of-the-art performance on multiple retinal image benchmarks.

Significantly improves alignment accuracy on challenging SFI-UWFI pairs.

Demonstrates effectiveness in real-world clinical scenarios.

Abstract

We propose a robust alignment technique for Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which are challenging to align due to differences in scale, appearance, and the scarcity of distinctive features. Our method, termed Particle Diffusion Matching (PDM), performs alignment through an iterative Random Walk Correspondence Search (RWCS) guided by a diffusion model. At each iteration, the model estimates displacement vectors for particle points by considering local appearance, the structural distribution of particles, and an estimated global transformation, enabling progressive refinement of correspondences even under difficult conditions. PDM achieves state-of-the-art performance across multiple retinal image alignment benchmarks, showing substantial improvement on a primary dataset of SFI-UWFI pairs and demonstrating its effectiveness in real-world clinical scenarios. By providing accurate and scalable correspondence estimation, PDM overcomes the limitations of existing methods and facilitates the integration of complementary retinal image modalities. This diffusion-guided search strategy offers a new direction for improving downstream supervised learning, disease diagnosis, and multi-modal image analysis in ophthalmology.