Choong Y F, Rakebrandt F, North R V, Morgan J E
Retinal Imaging Laboratory, Department of Optometry and Vision Sciences, Cardiff University, PO Box 905, Cardiff CF10, United Kingdom.
Br J Ophthalmol. 2003 Mar;87(3):322-6. doi: 10.1136/bjo.87.3.322.
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.
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.
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.
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.
背景/目的:青光眼患者高对比度视网膜神经纤维层(RNFL)图像的解读可能会因图像模糊而受到干扰;在质量较差的图像中,很难辨别出弥漫性轴突丢失。一种解决方案是基于RNFL以外的图像特征提供图像质量的客观测量方法。在本研究中,作者开发了一种客观方法来量化RNFL图像的清晰度,并将其与主观图像分级系统进行比较。
使用Topcon 50 IX视网膜相机从58只眼睛(每只眼睛一张图像)获取数字采集的单色视网膜图像。图像分辨率为1320×1032像素,每像素8位。由两名经验丰富的蒙面观察者相对于一组RNFL参考图像,主观地将图像清晰度分为1至5级。使用Matlab(5.2)开发软件算法,以计算锐度,这是一种客观测量方法,用于衡量构成图像锐度主观印象的物理特征。
所有图像均可计算出锐度值。每张图像锐度的对数与观察者1和观察者2的主观分级之间的Pearson相关系数分别为0.90(p<0.001,n = 58)和0.84(p<0.001,n = 58)。
这些数据表明,锐度可能为图像质量提供一种有用的客观测量方法,与数字视网膜图像锐度的主观印象密切相关。图像质量的客观测量方法应有助于区分弥漫性青光眼损害患者中弥漫性视网膜神经纤维丢失与图像模糊。