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使用数字图像分析对角膜染色进行客观评估。

Objective assessment of corneal staining using digital image analysis.

作者信息

Chun Yeoun Sook, Yoon Woong Bae, Kim Kwang Gi, Park In Ki

机构信息

Department of Ophthalmology, Chung-Ang University College of Medicine, Chung-Ang University Hospital, Seoul, South Korea.

Biomedical Engineering Branch, Division of Convergence Technology, National Cancer Center, Goyang, South Korea.

出版信息

Invest Ophthalmol Vis Sci. 2014 Nov 18;55(12):7896-903. doi: 10.1167/iovs.14-15618.

Abstract

PURPOSE

To validate a new objective digital image analysis technique to evaluate corneal staining.

METHODS

One hundred photographs of corneal staining from various ocular surface diseases in 100 patients were quantified by a new strategy: a combination of the difference of Gaussians (DoG) edge detection for morphologic properties of corneal erosions and the red-green-blue (RGB) systems and hue-saturation-value (HSV) color model for detection of color. To enhance the image, we adopted a median filter, Otsu thresholding, and contrast-limited adaptive histogram equalization (CLAHE). To validate the diagnostic value of this new strategy, the same photographs were also graded by two independent clinicians using the Oxford scheme and the National Eye Institute/Industry (NEI)-recommended guidelines. The correlation between the average subjective grade and objective image analysis measurement was evaluated using the Pearson's correlation coefficient.

RESULTS

The new algorithm showed a strong correlation with the clinical grading scale in the Oxford scheme and the NEI-recommended guidelines (R = 0.850 and 0.903, P < 0.001, respectively). The repeatability of the objective measurement was excellent (R = 0.994).

CONCLUSIONS

The new algorithm showed excellent correlation with the traditional subjective clinical grading scales. It may be useful for objective assessment of corneal staining, independent of disease conditions.

摘要

目的

验证一种用于评估角膜染色的新型客观数字图像分析技术。

方法

采用一种新策略对100例患者各种眼表疾病的100张角膜染色照片进行量化:利用高斯差分(DoG)边缘检测来分析角膜糜烂的形态学特征,并结合红-绿-蓝(RGB)系统和色相-饱和度-明度(HSV)颜色模型来检测颜色。为增强图像效果,采用了中值滤波、大津阈值法和对比度受限自适应直方图均衡化(CLAHE)。为验证该新策略的诊断价值,两名独立的临床医生还按照牛津方案和美国国立眼科研究所/行业(NEI)推荐的指南对相同照片进行了分级。使用Pearson相关系数评估平均主观分级与客观图像分析测量结果之间的相关性。

结果

新算法与牛津方案和NEI推荐指南中的临床分级量表显示出很强的相关性(分别为R = 0.850和0.903,P < 0.001)。客观测量的重复性极佳(R = 0.994)。

结论

新算法与传统主观临床分级量表显示出极佳的相关性。它可能有助于独立于疾病状况对角膜染色进行客观评估。

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