# Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement

arXiv: https://arxiv.org/abs/1803.00951 · HTML: https://mlsanity.com/paper/1803.00951/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration](https://mlsanity.com/paper/2109.02722/) (2021)
- [MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences](https://mlsanity.com/paper/2309.14550/) (2023)
- [ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration](https://mlsanity.com/paper/2401.05901/) (2024)
- [Medical Image Registration and Its Application in Retinal Images: A Review](https://mlsanity.com/paper/2403.16502/) (2024)

## Abstract (arXiv)

The analysis of different image modalities is frequently performed in ophthalmology as it provides complementary information for the diagnosis and follow-up of relevant diseases, like hypertension or diabetes. This work presents a hybrid method for the multimodal registration of color fundus retinography and fluorescein angiography. The proposed method combines a feature-based approach, using domain-specific landmarks, with an intensity-based approach that employs a domain-adapted similarity metric. The methodology is tested on a dataset of 59 image pairs containing both healthy and pathological cases. The results show a satisfactory performance of the proposed combined approach in this multimodal scenario, improving the registration accuracy achieved by the feature-based and the intensity-based approaches.