Automated Diagnosis of Glaucoma Using Digital Fundus Images
Automated Diagnosis of Glaucoma Using Digital Fundus Images
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DOI：
10.1007/s10916-008-9195-z
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发表时间：
2009-10-01
影响因子：
5.3
通讯作者：
Lim, Teik-Cheng
中科院分区：
文献类型：
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作者：
Nayak, Jagadish;Acharya, Rajendra U.;Lim, Teik-Cheng
青光眼是一种由眼内压升高引起的视神经疾病。青光眼主要通过增大视杯大小影响视盘。如不及时发现和治疗，可导致失明。通过光学相干断层扫描（OCT）和海德堡视网膜断层扫描（HRT）检测青光眼是非常昂贵的。提出了一种基于数字眼底图像的青光眼检测方法。数字图像处理技术，如预处理、形态学运算和阈值化，广泛应用于视盘、血管的自动检测和特征计算。我们提取的特征，如杯盘比（c/d），视盘中心和视神经乳头之间的距离与视盘直径的比值，和血管面积的比例在鼻颞侧的血管上的侧。这些功能是通过使用神经网络分类器分类正常和青光眼图像进行验证。本文提出的结果表明，这些特征在青光眼的检测中具有临床意义。我们的系统能够自动分类青光眼的敏感性和特异性分别为100%和80%。
Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity of 100% and 80% respectively.