Survey on a Hybrid Approach of Detection of Glaucoma Using Sdc Based Method and Lbp Features

Glaucoma is an extreme eye illness that prompts visual deficiency by expansion in weight at the dividers of the front eye. The glaucoma screening has become a research point in the field of biomedical image processing. The glaucoma detection is very essential factor in preventing the loss of eye sight. At the present day glaucoma screening done for 2D retinal fundus images. This paper addresses the survey on the method called “Container to glass” ratio which is very reliable in detecting the status of the progression of glaucoma disease. It classifies the input image into categories of the disease like normal, moderate and medium based on the computed value of the CGR. The CGR is characterized as the proportion of vertical container distance across to the vertical plate breadth. For control of CGR past calculations like territory of the circle, plate location, extraction of container and the extraction of circle must be finished. Consequently glaucoma screening by container to plate proportion will be extremely proficient technique for screening in vast populace based frameworks. Self assessed disk segmentation is the methodology which consolidates the superpixel division, edge recognition and round hough change. Superpixel era is done utilizing SLIC calculation that makes collection of pixel to frame a superpixel. Edge location is essential element for the identification of the region of container and the plate in the division process. Round about hough change which is an element extraction system used to distinguish the state of the sporadic data picture. From these information we can register glass to circle proportion that is the proportion of vertical container to the vertical plate. It will classify the input image into normal, medium and severe cases. This will be very helpful for the patients to know that in which stage the disease is progressed and they can take some preventive measure against vision loss.