Glaucomatous image classification: A review

Glaucoma is a progressive and chronic disease in which the retinal nerve that links to the brain is increasingly damaged that leads to the loss of vision which can ultimately cause blindness to the structure of retina. The detection and identification of glaucoma in retinal fundus image is important for preventing from damage of the vision. The early detection and cure is very essential so that it can be reduced by the treatment else it will lead to loss of vision and blindness. The discrimination of glaucoma in retinal images is the study of many researches in the field of biomedical image processing. In this article, we review various classification techniques for spontaneous detection of retina images which plays a vital role for timely and accurate glaucoma detection as a screening tool based on the cup to disc ratio (CDR) evaluation of pre-processing images. The significant image processing techniques are image registration, image fusion, image segmentation, feature extraction, image improvement, morphology, pattern recognition, image classification, analysis and statistical dimensions. The main study in this paper is to demonstrate a system which is mainly based on classification methods and image processing for detection of glaucoma by relating to various features of retina images of the glaucoma concerned patients.