Computerized Detection of Retinal Nerve Fiber Layer Defects in Retinal Fundus Images by Modified Polar Transformation and Gabor Filtering

Abstract

Glaucoma is the second leading cause of vision loss in the world. Detection of retinal nerve fiber layer defects (NFLDs), which is one of the early glaucomatous changes, on retinal fundus images obtained in mass screening may prevent patients from becoming permanently blind. In this study, a technique for contrast enhancement and automated detection of NFLDs was investigated. Images used in this study were obtained from the Tajimi general screening database, which includes retinal fundus images obtained in the population glaucoma screening study at Tajimi, Japan. In this preliminary investigation, images with at least one identified NFLD were included. Lesions of NFLDs were individually marked by two ophthalmologists, and those identified by both ophthalmologists were considered as the target NFLDs. First, the blood vessel regions in retinal fundus images were identified and interpolated by the surrounding pixels for creating “blood-vessel-erased” images. The resulted color images were red and blue-freed and were transformed from the Cartesian coordinate system to a modified polar coordinate system based on a set of parabolic lines passing through the center of optic nerve head. By applying the Gabor filter, the contrast of NFLDs was enhanced, and candidate regions for NFLDs were detected. The simple image features, such as the areas, mean pixel values, and contrast, of the candidates were determined for the false positive reduction. For 95 NFLDs identified on 80 retinal fundus images, the proposed technique detected 86% of NFLDs when the number of false positives was 1.3 per image. Further evaluation is needed for testing on independent database and images without NFLDs. The proposed technique can be useful for computer-aided detection of NFLDs.