ArticleBiomedical optics express2021
Hybrid registration of retinal fluorescein angiography and optical coherence tomography images of patients with diabetic retinopathy.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.BMC ophthalmology · 2022Trial
- Controllable editing via diffusion inversion on ultra-widefield fluorescein angiography for the comprehensive analysis of diabetic retinopathy.Biomedical optics express · 2024Article
- Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation.Biomedical optics express · 2023Article
- The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration.Sensors (Basel, Switzerland) · 2023Article
- Article
- Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.Journal of healthcare engineering · 2023Review
- Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Diabetic retinopathy (DR) is a common ophthalmic disease among diabetic patients. It is essential to diagnose DR in the early stages of treatment. Various imaging systems have been proposed to detect and visualize retina diseases. The fluorescein angiography (FA) imaging technique is now widely used as a gold standard technique to evaluate the clinical manifestations of DR. Optical coherence tomography (OCT) imaging is another technique that provides 3D information of the retinal structure. The FA and OCT images are captured in two different phases and field of views and image fusion of these modalities are of interest to clinicians. This paper proposes a hybrid registration framework based on the extraction and refinement of segmented major blood vessels of retinal images. The newly extracted features significantly improve the success rate of global registration results in the complex blood vessel network of retinal images. Afterward, intensity-based and deformable transformations are utilized to further compensate the motion magnitude between the FA and OCT images. Experimental results of 26 images of the various stages of DR patients indicate that this algorithm yields promising registration and fusion results for clinical routine.
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Registered trials
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