Autonomous Glaucoma detection from fundus image using cup to disc ratio and hybrid features

Glaucoma is a non-curable optic disease which can cause irreversible blindness if not detected at early stage. Progression of glaucoma occurs due to an increase in intraocular pressure and results in the damage of optic nerve. Progression of glaucoma can be stopped if detected at an early stage. There are no early symptoms of glaucoma and the only source to detect glaucoma at an early stage is the structural change that arises in the internal eye. Fundoscopy is one of the modern medical imaging techniques that enable Ophthalmologists to observe structural changes in the Optic Disc to detect glaucoma. Many autonomous glaucoma detection systems analyze fundus image by calculating Cup to Disc Ratio (CDR) and categorize the image as glaucoma or healthy. Glaucoma detection using machine learning is also being used widely to aid ophthalmologists. The proposed methodology provides a novel algorithm to detect glaucoma using a fusion of CDR and hybrid textural and intensity features. Image categorization (glaucoma, non-glaucoma, suspect) is done based on the results from both CDR and classifier. This fusion of CDR with hybrid features has improved the sensitivity of system to 1, specificity 0.88 and accuracy 92%.