为了自动获取所需医学参数，辅助医生诊断，提出了一种基于边缘检测和随机抽样一致性的中央角膜厚度自动测量方法. 采用边缘检测算子获得眼前节组织光学相干层析图像中的初始边缘，然后利用随机抽样一致性算法对初始中央角膜上边缘进行圆弧拟合，进一步提取中央角膜下边缘并采用相同方法进行圆弧拟合，根据得到的中央角膜上下边缘计算中央角膜厚度. 实验结果表明，该算法能排除图像中时常出现的中央亮线干扰，实时而准确地提取中央角膜上下边缘，得到的中央角膜厚度计算结果与人工分析基本一致，具有良好的应用价值和商业前景.
To obtain quantitative parameters automatically and help medical diagnosis, automatic measurement of central cornea thickness based on edge detection and random sample consensus (RANSAC) is employed. The initial edge in the eye anterior segment optical coherence tomography (OCT) image is obtained with an edge detector. Upper and lower edges of the central cornea are extraxted using the RANSAC circle fitting method. The central cornea thickness is then computed based on the edges. Experiments show that the proposed method can avoid the effect of light beam crossing, and good results comparable to manual analysis can be obtained in real time, indicating that the method has potential applications in the future.
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