Automated Glaucoma Detection Using Global Statistical Parameters of Retina Fundus Images

Abstract

Glaucoma is an eye disorder which is prevalent in the ageing population and causes irreversible loss of vision. Hence, computer-aided solutions are of interest for screening purposes. Glaucoma is indicated by structural changes in the Optic Disc (OD), loss of nerve fibres and atrophy of the peripapillary region of optic disc in retina. In retina images, most changes appear in form of subtle variation in appearance. Hence, automated assessment of glaucoma from colour fundus images is a challenging problem. Prevalent approaches aim at detecting the primary indicator, namely, the optic cup deformation relative to the disc and use the ratio of the two diameters in the vertical direction, to classify images as normal or glaucomatous. An attempt is made to detect glaucoma by combining image processing and neural network techniques. The risk of blindness can be reduced by 50% with screening patients vulnerable to eye diseases specially glaucoma. The global statistical features of the dataset images are used to detect images as glaucoma or normal. The technique involves screening for the vital signs such as intensity values in the fundus image for detecting glaucoma in patients. The result shows the feasibility of detection of glaucoma for vulnerable patient.