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

TL;DR

This paper introduces a hybrid multimodal retinal image registration method combining domain-specific landmarks and intensity-based similarity metrics, improving accuracy in aligning fundus and angiography images for ophthalmology diagnostics.

Contribution

It presents a novel combined approach for multimodal retinal image registration using domain-specific landmarks and vessel enhancement, outperforming individual methods.

Findings

Improved registration accuracy over standalone methods

Effective on both healthy and pathological cases

Demonstrated on a dataset of 59 image pairs

Abstract

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.