Assessment of early hypertensive retinopathy using fractal analysis of retinal fundus image

Abstract

Hypertensive retinopathy is characterized by changes in retinal vessels, a change known as tortuosity. Automated analysis of retinal vascular changes will make it easier for clinicians to make an initial diagnosis of the disease. The pattern of blood vessels in the retina of the eye can be approached with a fractal pattern. This study proposes a method for the early detection of disease hypertensive retinopathy using the fractal analysis approach fundus retinal image. Variable fractal used is the fractal dimension and lacunarity, whereas for the classification algorithm using ensemble Random Forest and validation using the k-fold cross-validation. Performance measurement using the parameters of accuracy, positive prediction value (PPV), negative prediction value (NPV), sensitivity, specificity and area under the curve (AUC). The test results using 10-fold cross-validation values obtained accuracy 88.0%, PPV 84.0%, NPV 92.0%, sensitivity 91.3%, specificity 85.19%, and 88.25% AUC. The performance is produced when using lacunarity the box size 22. Based on the research results, it can be concluded that early detection of hypertensive retinopathy with fractal analysis approaches have a performance based on AUC produced included in good categories.