A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images

Leontidis, Georgios and Al-Diri, Bashir and Hunter, Andrew (2017) A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images. Computers in Biology and Medicine, 90 . pp. 98-115. ISSN 0010-4825

Full content URL: https://doi.org/10.1016/j.compbiomed.2017.09.008

Leontidis_CBM2017.pdf - Whole Document

Item Type:Article
Item Status:Live Archive


Human retina is a diverse and important tissue, vastly studied for various retinal and other diseases. Diabetic retinopathy (DR), a leading cause of blindness, is one of them. This work proposes a novel and complete framework for the accurate and robust extraction and analysis of a series of retinal vascular geometric features. It focuses on studying the registered bifurcations in successive years of progression from diabetes (no DR) to DR, in order to identify the vascular alterations. Retinal fundus images are utilised, and multiple experimental designs are employed. The framework includes various steps, such as image registration and segmentation, extraction of features, statistical analysis and classification models. Linear mixed models are utilised for making the statistical inferences, alongside the elastic-net logistic regression, boruta algorithm, and regularised random forests for the feature selection and classification phases, in order to evaluate the discriminative potential of the investigated features and also build classification models. A number of geometric features, such as the central retinal artery and vein equivalents, are found to differ significantly across the experiments and also have good discriminative potential. The classification systems yield promising results with the area under the curve values ranging from 0.821 to 0.968, across the four different investigated combinations

Keywords:Framework, Diabetic Retinopathy, Statistical Analysis, Detection, Machine Learning, Classification
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:28720
Deposited On:25 Oct 2017 13:47

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