Automated Detection of Leakage in Fluorescein Angiography Images with Application to Malarial Retinopathy

Zhao, Yitian, MacCormick, Ian JC, Parry, David ORCID: 0000-0002-9114-774X, Leach, Sophie, Beare, Nicholas AV ORCID: 0000-0001-8086-990X, Harding, Simon ORCID: 0000-0003-4676-1158 and Zheng, Yalin ORCID: 0000-0002-7873-0922 (2015) Automated Detection of Leakage in Fluorescein Angiography Images with Application to Malarial Retinopathy. Scientific Reports, 5 (1). 10425-.

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Official URL: https://www.nature.com/articles/srep10425

Abstract

The detection and assessment of leakage in retinal fluorescein angiogram images is important for the management of a wide range of retinal diseases. We have developed a framework that can automatically detect three types of leakage (large focal, punctate focal, and vessel segment leakage) and validated it on images from patients with malarial retinopathy. This framework comprises three steps: vessel segmentation, saliency feature generation and leakage detection. We tested the effectiveness of this framework by applying it to images from 20 patients with large focal leak, 10 patients with punctate focal leak, and 5,846 vessel segments from 10 patients with vessel leakage. The sensitivity in detecting large focal, punctate focal and vessel segment leakage are 95%, 82% and 81%, respectively, when compared to manual annotation by expert human observers. Our framework has the potential to become a powerful new tool for studying malarial retinopathy, and other conditions involving retinal leakage.

Item Type:	Article
Uncontrolled Keywords:	Retinal Vessels, Humans, Malaria, Retinal Diseases, Fluorescein Angiography, Sensitivity and Specificity, Reproducibility of Results, Capillary Permeability
Depositing User:	Symplectic Admin
Date Deposited:	17 Jul 2015 11:01
Last Modified:	16 Dec 2022 15:00
DOI:	10.1038/srep10425
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/2016819