Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement
This work addresses the problem of aligning different retinal image modalities for ophthalmologists, but it is incremental as it builds on existing approaches.
The paper tackled multimodal registration of color fundus retinography and fluorescein angiography by combining feature-based and intensity-based approaches, resulting in improved registration accuracy over individual methods on a dataset of 59 image pairs.
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.