Evaluation of geometric features as biomarkers of diabetic retinopathy for characterizing the retinal vascular changes during the progression of diabetes

Leontidis, Georgios and Al-Diri, Bashir and Wigdahl, Jeffrey and Hunter, Andrew (2015) Evaluation of geometric features as biomarkers of diabetic retinopathy for characterizing the retinal vascular changes during the progression of diabetes. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 25-29 August, 2015, Milan, Italy.

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Item Type:Conference or Workshop contribution (Paper)
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Abstract

Diabetic retinopathy (DR) has been widely studied and characterized. However, until now, it is unclear how different features, extracted from the retinal vasculature, can be associated with the progression of diabetes and therefore become biomarkers of DR. In this study, a comprehensive analysis is presented, in which four groups were created, using eighty fundus images from twenty patients, who have progressed to DR and they had no history of any other diseases (e.g. hypertension or glaucoma). The significance of the following features was evaluated: widths, angles, branching coefficient (BC), angle-to-BC ratio, standard deviations, means and medians of widths and angles, fractal dimension (FD), lacunarity and FD-to-lacunarity ratio, using a mixed model analysis of variance (ANOVA) design. All the features were measured from the same junctions of each patient, using an automated tool. The discriminative power of these features was evaluated, using decision trees and random forests classifiers. Cross validation and out-of-bag error were used to evaluate the classifiers’ performance, calculating the area under the ROC curve (AUC) and the classification error. Widths, FD and FD- to-Lacunarity ratio were found to differ significantly. Random forests had a superior performance of 0.768 and 0.737 in the AUC for the two cases of classification, namely three-years-pre- DR/post-DR and two-years-pre-DR/post-DR respectively.

Keywords:diabetic retinopathy, Machine Learning, Statistical inference, diabetes mellitus, Retina
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G300 Statistics
Divisions:College of Science > School of Computer Science
ID Code:19001
Deposited On:13 Oct 2015 19:28

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