Feature and Segmentation Based Eye Disease Classification Using Glcm and Tree Technique

Abstract

 Now-a-days, Medical imaging and its analysis is a very notable area in research and development where digital images are processed for the diagnosis and helps in identifying different medical oriented issues. Diabetic retinopathy (DR) is a kind of the serious eye problems that may leads to blindness. DR is a disease related to eye caused by the increase of insulin in blood. The earlier identification and prediction of the disease DR helps in saving a patient vision and also helps to find abnormalities like different types of lesion, such as hemorrhages, micro aneurysms, soft and hard exudates. DR is a threatening disease to vision due to diabetes mellitus which is the important reason for loss of vision. In frequent situations the patient is not conscious of  the disease until it is delay for effective treatment. The prevalence of retinopathy varies with the age of diabetes and the time of disease. Early diagnosis by regular checkup screening and treatment helps in preventing from impairment and loss of vision. In this paper, presented a method for detecting and classifying the types of abnormality that present in retinal images and calculate accuracy performance. Various image processing methodologies including Image Acquisition, Filtering, Enhancement, Segmentation, Classification and concept has been improved for the prediction of DR earlier based on abnormalities in human eye. It projects a review of work on using of processing techniques for detecting DR using Grey Level Co-occurrence Matrix (GLCM).