Identifying Diabetic Retinopathy Using Retinal Fundus Images

Abstract

Medical image analysis is one of the research areas that are currently attracting intensive Interests of scientists and physicians. It consists of the study of digital images with the objective of providing computational tools that assist quantification and visualization of interesting pathology and anatomical structures. The progress, which has been achieved in this area over recent years, has significantly improved the type of medical care that is available to patients. Nowadays physicians can examine inside the human body to diagnose, treat, monitor changes and analyze more successfully than before. However, this is a multidisciplinary task and requires comprehensive knowledge in many disciplines, such as image processing and computer vision, machine learning, pattern recognition and expert systems. The severe progression of diabetes is one of the greatest immediate challenges to current health care. It leads to severe late complications including macro and micro vascular changes resulting in heart disease and retinopathy. Diabetic Retinopathy (DR) is one of the common causes for blindness in the working population of western countries. However, only one half of the patients are aware of the disease. Diabetic-related eye diseases are major causes of preventable blindness in the world. It is a complication of diabetes which can also affect various parts of the body. When the small blood vessels have a high level of break down product of glucose in the retina, the vision will be blurred and small blood vessels damage eventually. This is known as diabetic retinopathy. This project results such as the structure of blood vessels, microaneurysms, exudates and using image processing techniques. These features are input into artificial neural network for automatic detection and can quickly process a large number of fundus images obtained from mass screening to help by reducing the cost, increases productivity and efficiency of ophthalmologists.