A New Approach for Detection of Blood Vessel Tracking System and Glaucoma for Diabetic Patient

Glaucoma is a disease affected by an eye which results normally and impaired by the normal optic nerve. It is caused by increase in the pressure which utilize across the outer coats of the eye ball. Glaucoma primarily causes a retinal vessel occlusion and then it alters the optic disc by getting higher the cup size. The blindness will lead to causes and it will detect some problems and treated in a convinced time period. In this proposed method, by using retinal fundus image the green channel is used to extract the optic cup and the optic disk. Circular Hough transformation is used for the feature extraction which is used to detect the circles in imperfect images in this method. Different methods can be used in this proposed method such as preprocessing, thresholding and histogram equalization which is used for the automated detection of blood vessel. The extracted features can be used to find the cup to disk ratio (CDR), if the CDR value is more than 0.3 it indicates the severe stage of glaucoma can be detected in the patients and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm can be used to separate the normal eye and glaucoma eye from the retinal fundus image and neural network classifier.