Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma

Abstract

Retinal nerve fiber layer defect (NFLD) is a major sign of glaucoma, which is the second leading cause of blindness in the world. Early detection of NFLDs is critical for improved prognosis of this progressive, blinding disease. We have investigated a computerized scheme for detection of NFLDs on retinal fundus images. In this study, 162 images, including 81 images with 99 NFLDs, were used. After major blood vessels were removed, the images were transformed so that the curved paths of retinal nerves become approximately straight on the basis of ellipses, and the Gabor filters were applied for enhancement of NFLDs. Bandlike regions darker than the surrounding pixels were detected as candidates of NFLDs. For each candidate, image features were determined and the likelihood of a true NFLD was determined by using the linear discriminant analysis and an artificial neural network (ANN). The sensitivity for detecting the NFLDs was 91% at 1.0 false positive per image by using the ANN. The proposed computerized system for the detection of NFLDs can be useful to physicians in the diagnosis of glaucoma in a mass screening.