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A novel approach to detect glaucoma in retinal fundus images using cup-disk and rim-disk ratio

机译:利用杯盘和边缘盘比率检测视网膜眼底图像中的青光眼的新方法

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Glaucoma is a chronic disease which if not detected in early stages can lead to permanent blindness. The medical techniques used by ophthalmologists like HRT and OCT is costly and time consuming. Hence there is a need to develop automatic computer aided system which can detect glaucoma efficiently and in less time. Optic disk and optic cup are prime features which help in diagnosing glaucoma. Thus proper segmentation of optic disk and optic cup plays an important role in detecting the disorder. In this paper an adaptive threshold based method which is independent of image quality and invariant to noise is used to segment optic disk, optic cup, Neuroretinal rim and cup to disk ratio is calculated to screen glaucoma. Another ocular parameter, rim to disk ratio is also considered which in combination with CDR gives more reliability in determining glaucoma and makes the system more robust. Further an SVM classifier has been used to categorize the images as glaucomatic or non glaucomatic. The experimental results obtained are compared with those of ophthalmologist and are found to have high accuracy of 90%. Also in addition, the proposed method is faster having low computational cost.
机译:青光眼是一种慢性疾病,如果不能在早期发现,可能会导致永久性失明。 HRT和OCT等眼科医生使用的医疗技术既昂贵又耗时。因此,需要开发一种可以在短时间内有效地检测青光眼的自动计算机辅助系统。视盘和视杯是有助于诊断青光眼的主要功能。因此,视盘和视杯的适当分割在检测疾病中起着重要的作用。本文采用一种基于自适应阈值的方法,该方法不受图像质量的影响,并且不受噪声的影响,用于分割视盘,视杯,神经视网膜边缘以及杯盘比以筛查青光眼。还考虑了另一个眼部参数,即眼缘与椎间盘的比率,与CDR结合使用时,在确定青光眼方面具有更高的可靠性,并使系统更加坚固。另外,已经使用SVM分类器将图像分类为青光眼的或非青光眼的。将获得的实验结果与眼科医生进行比较,发现具有90%的高精度。另外,所提出的方法具有较低的计算成本,速度更快。

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