Computer aided diagnosis of ocular health based on hybrid feature selection and classification

Abstract

Glaucoma and diabetic retinopathy (DR) are the ocular disorder newlinethat causes irreversible loss of vision. At present, ocular diseases are graded newlinemanually, which is time-consuming, labor-intensive and the diagnosis can be newlinesubjective. The use of a computer-aided diagnosis (CAD) system will reduce newlinethe time consumed for analysis and will clarify the inter-observer reliability in newlineimage interpretation. The focus of this thesis is to develop a CAD system for newlinethe early detection of ocular diseases such as glaucoma and DR. newlineGlaucoma is a slowly progressive disease that damages the newlinestructural appearance of the optic nerve head without any early symptoms. newlineCharacterization of glaucoma can be done by extracting discriminate features newlinelike blood vessel ratio (BVR), cup to disc ratio (CDR), disc damage likelihood newlinescale (DDLS) and neuroretinal rim (NRR) area from the fundus image. DR is newlineassociated with diabetics that damage the blood vessels which may lead to newlinevision problems. They can be characterized by detecting the blood vessels newlinefollowed by the detection of exudates. The analysis of glaucoma and DR is newlineimportant to prevent earlier vision loss. newlineThe main objective of this research is to predict the potential newlinefeatures from retinal image analysis for the assessment of ocular pathologies newlinesuch as glaucoma and DR. newline