Georgios Leontidis

Diabetic retinopathy: current and future methods for early screening from a retinal hemodynamic and geometric approach

Diabetic retinopathy (DR) is a major disease and is the number one cause of blindness in the UK. In England alone, 4200 new cases appear every year and 1280 lead to blindness. DR is a result of diabetes mellitus, which affects the retina of the eye and specifically the vessel structure. Elevated levels of glucose cause a malfunction in the cell structure, which affects the vessel wall and, in severe conditions, leads to their breakage. Much research has been carried out on detecting the different stages of DR but not enough versatile research has been carried out on the detection of early DR before the appearance of any lesions. In this review, the authors approach the topic from the functional side of the human eye and how hemodynamic factors that are impaired by diabetes affect the vascular structure.

Exploiting the Retinal Vascular Geometry in Identifying the Progression to Diabetic Retinopathy Using Penalized Logistic Regression and Random Forests

Many studies have been conducted, investigating the effects that diabetes has to the retinal vasculature. Identifying and quantifying the retinal vascular changes remains a very challenging task, due to the heterogeneity of the retina. Monitoring the progression requires follow-up studies of progressed patients, since human retina naturally adapts to many different stimuli, making it hard to associate any changes with a disease. In this novel study, data from twenty five diabetic patients, who progressed to diabetic retinopathy, were used. The progression was evaluated using multiple geometric features, like vessels widths and angles, tortuosity, central retinal artery and vein equivalent, fractal dimension, lacunarity, in addition to the corresponding descriptive statistics of them. A statistical mixed model design was used to evaluate the significance of the changes between two periods: 3 years before the onset of diabetic retinopathy and the first year of diabetic retinopathy. Moreover, the discriminative power of these features was evaluated using a random forests classifier and also a penalized logistic regression. The area under the ROC curve after running a ten-fold cross validation was 0.7925 and 0.785 respectively.

Summarising the retinal vascular calibres in healthy, diabetic and diabetic retinopathy eyes

Retinal vessel calibre has been found to be an important biomarker of several retinal diseases, including diabetic retinopathy (DR). Quantifying the retinal vessel calibres is an important step for estimating the central retinal artery and vein equivalents. In this study, an alternative method to the already established branching coefficient (BC) is proposed for summarising the vessel calibres in retinal junctions. This new method combines the mean diameter ratio with an alternative to Murray׳s cube law exponent, derived by the fractal dimension,experimentally, and the branch exponent of cerebral vessels, as has been suggested in previous studies with blood flow modelling. For the above calculations, retinal images from healthy, diabetic and DR subjects were used. In addition, the above method was compared with the BC and was also applied to the evaluation of arteriovenous ratio as a biomarker of progression from diabetes to DR in four consecutive years, i.e. three/two/one years before the onset of DR and the first year of DR. Moreover, the retinal arteries and veins around the optic nerve head were also evaluated. The new approach quantifies the vessels more accurately. The decrease in terms of the mean absolute percentage error was between 0.24% and 0.49%, extending at the same time the quantification beyond healthy subjects.

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

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.

Automatic Gunn and Salus sign quantification in retinal images

Prolonged hypertension can lead to abnormal changes in the retinal vasculature, including sclerosis and thickening of the arteriole walls. These changes can cause compression (Gunns sign) and deflection (Saluss sign) of the veins at arteriovenous crossings. In retinal images, Gunns sign appears as a tapering of the vein at a crossing point, while Saluss sign presents as an S-shaped curving. This paper presents a method for the automatic quantification of these two signs once a crossover has been detected; combining segmentation, artery vein classification, and morphological feature extraction techniques to calculate vein widths and angles entering and exiting the crossover. The method was tested on a small set of crossings, graded by a set of 3 doctors who were in agreement as having or not having Gunn/Salus sign. Results show separation between the two classes and that we can reliably detect and quantify these sign under the right conditions.