A Generalized Grayscale Image Processing Framework for Retinal Fundus Images

Abstract

Diabetic Retinopathy (DR) is a debilitating ocular complication of diabetes that results from prolonged exposure of the retina to elevated levels of blood glucose. This exposure can lead to progressive microvascular changes and neuronal injury, resulting in a spectrum of visual impairments ranging from mild vision changes to severe vision loss and blindness. DR typically manifests as structural changes in the blood vessels of the retina, including capillary non-perfusion, microaneurysms, retinal hemorrhages, and new vessel formation. DR is challenging to diagnose and treat due to the gradual onset of symptoms and the lack of early warning signs. Therefore, regular eye exams are critical for early detection and management of DR. A human ophthalmologist would take a significant amount of time, based on their ability and experience, to go through the fundus image and diagnose DR. Despite advancements in DR management, it remains a significant public health issue, and further research is essential to improve the understanding of DR in order to overcome the existing complications. This paper proposes a solution for improving retinal fundus images by creating more precise computerized image analysis medical diagnosis with fewer computational requirements as the images are grayscaled so that irrespective of the imaging apparatus the features of the images are enhanced without loss of information. The results of the proposed framework are assessed using entropy, contrast improvement index and structural similarity index measure.