BACKGROUND The Corvis® ST tonometer is an innovative device which, by combining a classic non-contact tonometer with an ultra-fast Scheimpflug camera, provides a number of parameters allowing for the assessment of corneal biomechanics. The acquired biomechanical parameters improve medical diagnosis of selected eye diseases. One of the key elements in biomechanical measurements is the correct corneal contour detection, which is the basis for further calculations. The presented study deals with the problem of outer corneal edge detection based on a series of images from the afore-mentioned device. Corneal contour detection is the first and extremely important stage in the acquisition and analysis of corneal dynamic parameters. RESULT A total of 15,400 images from the Corvis® ST tonometer acquired from 110 patients undergoing routine ophthalmologic examinations were analysed. A method of outer corneal edge detection on the basis of a series of images from the Corvis® ST was proposed. The method was compared with known and commonly used edge detectors: Sobel, Roberts, and Canny operators, as well as others, known from the literature. The analysis was carried out in MATLAB® version 9.0.0.341360 (R2016a) with the Image Processing Toolbox (version 9.4) and the Neural Network Toolbox (version 9.0). The method presented in this paper provided the smallest values of the mean error (0.16%), stability (standard deviation 0.19%) and resistance to noise, characteristic for Corvis® ST tonometry tests, compared to the methods known from the literature. The errors were 5.78 ± 9.19%, 3.43 ± 6.21%, and 1.26 ± 3.11% for the Roberts, Sobel, and Canny methods, respectively. CONCLUSIONS The proposed new method for detecting the outer corneal contour increases the accuracy of intraocular pressure measurements. It can be used to analyse dynamic parameters of the cornea.

中文翻译：

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一种从超快Scheimpflug相机检测图像中角膜外轮廓的新方法。

背景技术Corvis®ST眼压计是一种创新的设备，通过将经典的非接触式眼压计与超快的Scheimpflug相机相结合，提供了许多可用于评估角膜生物力学的参数。所获得的生物力学参数改善了所选眼部疾病的医学诊断。生物力学测量中的关键要素之一是正确的角膜轮廓检测，这是进一步计算的基础。提出的研究基于来自上述装置的一系列图像来解决角膜外边缘检测的问题。角膜轮廓检测是角膜动态参数获取和分析的第一个也是极其重要的阶段。结果总共15 分析了来自Corvis®ST眼压计的400幅图像，这些图像是从110名接受常规眼科检查的患者中获得的。提出了一种基于Corvis®ST系列图像的角膜外边缘检测方法。将该方法与已知的和常用的边缘检测器进行了比较：Sobel，Roberts和Canny运算符，以及文献中已知的其他检测器。在MATLAB®版本9.0.0.341360（R2016a）中使用图像处理工具箱（版本9.4）和神经网络工具箱（版本9.0）进行了分析。与文献中已知的方法相比，本文提供的方法提供了Corvis®ST眼压测试的特征值的平均值（0.16％），稳定性（标准偏差0.19％）和抗噪声性的最小值。误差为5.78±9.19％，3.43±6.21％和1。Roberts，Sobel和Canny方法分别为26±3.11％。结论提出的用于检测角膜外轮廓的新方法可提高眼压测量的准确性。它可以用来分析角膜的动态参数。