# Registration of retinal images from Public Health by minimising an error between vessels using an affine model with radial distortions

## TL;DR

This paper presents a novel retinal image registration method that models affine transformations with radial distortions, using vessel overlap errors, achieving high accuracy on public datasets and outperforming existing methods.

## Contribution

The paper introduces a new registration approach combining affine and radial distortion models, with vessel-based error minimization, improving accuracy over previous methods.

## Key findings

- Successfully registers 96% of image pairs in a public dataset
- Outperforms previous methods and state-of-the-art techniques
- Achieves better registration results than reference methods on public datasets

## Abstract

In order to estimate a registration model of eye fundus images made of an affinity and two radial distortions, we introduce an estimation criterion based on an error between the vessels. In [1], we estimated this model by minimising the error between characteristics points. In this paper, the detected vessels are selected using the circle and ellipse equations of the overlap area boundaries deduced from our model. Our method successfully registers 96 % of the 271 pairs in a Public Health dataset acquired mostly with different cameras. This is better than our previous method [1] and better than three other state-of-the-art methods. On a publicly available dataset, ours still better register the images than the reference method.