Automatic glaucoma detection using adaptive threshold based technique in fundus image

Glaucoma is a kind of ocular disorder that results in a damaged optic nerve which is responsible for transmitting images to the brain. The conventional methods to detect glaucoma like Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) are expensive and need specialized manpower. A digital fundus image can be used to identify glaucoma. This paper describes an efficient method to analyze a computer-aided fundus image which can act as a diagnostic tool for detection of glaucoma. A technique based on histogram of the image is used to study some statistical features of the image such as mean and standard deviation. A relationship between them is established to find a threshold value for segmenting optic disc and optic cup. An adaptive threshold based method which is independent of image quality and invariant to noise is used to segment the optic disc, optic cup and the cup-to-disc ratio CDR which is used to screen glaucoma. The experimental results obtained are compared with those of the ophthalmologist and are found to have a high accuracy. Also in addition the proposed method is efficient having a low computational cost.