Y F Choong1, F Rakebrandt, R V North, J E Morgan. 1. Retinal Imaging Laboratory, Department of Optometry and Vision Sciences, Cardiff University, PO Box 905, Cardiff CF10, United Kingdom.
Abstract
BACKGROUND/AIMS: The interpretation of high contrast retinal nerve fibre layer (RNFL) images in glaucoma can be confounded by the presence of image blur; it can be difficult to discern diffuse axon loss in a poor quality image. One solution is to provide an objective measure of the image quality based on features in the image other than the RNFL. In this study the authors have developed an objective method to quantify the clarity of RNFL images, comparing it with a subjective image grading system. METHODS: Digitally acquired, monochrome retinal images were taken from 58 eyes (one image per eye) with a Topcon 50 IX retinal camera. Image resolution was 1320 x 1032 pixels at 8 bits per pixel. Image sharpness was subjectively graded by two masked experienced observers on a scale 1 to 5 relative to a reference set of RNFL images. Software algorithms were developed using Matlab (5.2) to calculate the acutance, an objective measure of the physical characteristics that underlie the subjective impression of sharpness in an image. RESULTS: Acutance values could be calculated for all the images. The Pearson correlation coefficients of the log of the acutance for each image and the subjective grades of observer 1 and observer 2 were 0.90 (p<0.001, n=58) and 0.84 (p<0.001, n=58) respectively. CONCLUSIONS: These data suggest that acutance may provide a useful objective measure of image quality, which correlates well with the subjective impression of the digital retinal image sharpness. Objective measures of image quality should help in the discrimination of diffuse retinal nerve fibre loss from image blur in patients with diffuse glaucomatous damage.
BACKGROUND/AIMS: The interpretation of high contrast retinal nerve fibre layer (RNFL) images in glaucoma can be confounded by the presence of image blur; it can be difficult to discern diffuse axon loss in a poor quality image. One solution is to provide an objective measure of the image quality based on features in the image other than the RNFL. In this study the authors have developed an objective method to quantify the clarity of RNFL images, comparing it with a subjective image grading system. METHODS: Digitally acquired, monochrome retinal images were taken from 58 eyes (one image per eye) with a Topcon 50 IX retinal camera. Image resolution was 1320 x 1032 pixels at 8 bits per pixel. Image sharpness was subjectively graded by two masked experienced observers on a scale 1 to 5 relative to a reference set of RNFL images. Software algorithms were developed using Matlab (5.2) to calculate the acutance, an objective measure of the physical characteristics that underlie the subjective impression of sharpness in an image. RESULTS: Acutance values could be calculated for all the images. The Pearson correlation coefficients of the log of the acutance for each image and the subjective grades of observer 1 and observer 2 were 0.90 (p<0.001, n=58) and 0.84 (p<0.001, n=58) respectively. CONCLUSIONS: These data suggest that acutance may provide a useful objective measure of image quality, which correlates well with the subjective impression of the digital retinal image sharpness. Objective measures of image quality should help in the discrimination of diffuse retinal nerve fibre loss from image blur in patients with diffuse glaucomatous damage.
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