Early Diagonisis of glaucoma using fuzzy classification

Manual diagnosis needs a great deal of time for ophthalmologists to analyze and review retinal images of the eye obtained by fundus camera. Digital image processing techniques enable ophthalmologists to detect and treat several eye diseases like diabetic retinopathy and glaucoma. Glaucoma, the next common cause of blindness, is the disease of optic nerve of the eye and can lead to ultimate blindness. Raised intraocular pressure is the raised modifiable reason for Glaucoma. The main objective of this paper is to find an automated tool to detect Glaucoma at an early stage and classify it’s various stages. The objective of this paper is to study preprocessing of retinal fundus images enhancing the quality which is required for further processing and to design a novel algorithm to find the boundary region between optic disk and cup. The cup to disk ration ( CDR) is required to predict the disease at an early stage. Comparing automated detection to manual detection is also aim of this paper. This paper also concentrates on early detection of Glaucoma, along with fuzzy Classification