A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images for Detection of Hypertensive Retinopathy and Cardiovascular Diseases

Abstract

Quantitative studies for classification of retinal vessels using new computer-assisted retinal fundus imaging system have allowed the researchers to understand the influence of systemic on retinal vascular caliber. These retinal vascular caliber changes reflect the cumulative response to cardiovascular risk factor. Hypertensive retinopathy can be detected in earlier stage by analyzing the retinal image. Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and the most common diseases such as hypertension, stroke, cardiovascular diseases, those can be detected by noninvasive retinal fundus image. The proposed approach of applying an image processing technique, the aforementioned disease can be diagnosed earlier by retinal fundus image. To achieve the precise measurement of the retinal image parameters, the classification of blood vessels such as arteries and veins is necessary. These classifications of arteries and veins can be achieved through the retinal fundus image. The retinal vessel classification is based on visual and geometric features from these classified images into arteries and veins for the detection of hypertensive retinopathy, stroke, and cardiovascular risk factor. This classification of retinal fundus image is essential for early diagnosis of aforementioned diseases. The retinal arteriolar caliber which is narrower and smaller, that is associated with older age, will predict the incidence of diabetic retinopathy and cardiovascular risk factor. Similarly, retinal venular caliber which is wider, that is associated with younger age, will predict the incidence of risks of stroke and coronary heart diseases. This could suggest the possibility of using this model of fundus image in classification approaches. Finally, the selected attributes of classification are applied through the genetic algorithm with radial basis function neural network for diagnosis of the disease in order to improve the classification accuracy with less computational cost time.