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Comparison of optic nerve imaging methods to distinguish normal eyes from those with glaucoma.比较视神经成像方法以区分正常眼睛和青光眼患者的眼睛。
Invest Ophthalmol Vis Sci. 2002 Jan;43(1):140-5.
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Clinical ability of Heidelberg retinal tomograph examination to detect glaucomatous visual field changes.海德堡视网膜断层扫描仪检查检测青光眼性视野改变的临床能力。
Ophthalmology. 2001 Sep;108(9):1621-7. doi: 10.1016/s0161-6420(01)00676-5.
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Discriminating between normal and glaucomatous eyes using the Heidelberg Retina Tomograph, GDx Nerve Fiber Analyzer, and Optical Coherence Tomograph.使用海德堡视网膜断层扫描仪、GDx神经纤维分析仪和光学相干断层扫描仪鉴别正常眼睛和青光眼眼睛。
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Digital imaging and microtexture analysis of the nerve fiber layer.神经纤维层的数字成像与微观纹理分析
J Glaucoma. 2000 Feb;9(1):5-9. doi: 10.1097/00061198-200002000-00003.
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Identification of early glaucoma cases with the scanning laser ophthalmoscope.使用扫描激光检眼镜识别早期青光眼病例。
Ophthalmology. 1998 Aug;105(8):1557-63. doi: 10.1016/S0161-6420(98)98047-2.
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Evaluation of a reference set based grading system for retinal nerve fiber layer photographs in 1941 eyes.对1941只眼睛的视网膜神经纤维层照片基于参考集的分级系统进行评估。
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Measures of acutance and shape for classification of breast tumors.用于乳腺肿瘤分类的锐度和形状测量。
IEEE Trans Med Imaging. 1997 Dec;16(6):799-810. doi: 10.1109/42.650876.
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The sensitivity and specificity of nerve fiber layer measurements in glaucoma as determined with scanning laser polarimetry.
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Evaluation of the retinal nerve fiber layer.视网膜神经纤维层评估。
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锐度,一种视网膜神经纤维图像清晰度的客观测量指标。

Acutance, an objective measure of retinal nerve fibre image clarity.

作者信息

Choong Y F, Rakebrandt F, North R V, Morgan J E

机构信息

Retinal Imaging Laboratory, Department of Optometry and Vision Sciences, Cardiff University, PO Box 905, Cardiff CF10, United Kingdom.

出版信息

Br J Ophthalmol. 2003 Mar;87(3):322-6. doi: 10.1136/bjo.87.3.322.

DOI:10.1136/bjo.87.3.322
PMID:12598447
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1771537/
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.

摘要

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。

方法

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。

结果

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)。

结论

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。