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

机译:使用杯盘和边缘盘比检测视网膜眼镜图像中青光眼的新方法

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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,如果未在早期阶段检测到可能导致永久性失明。眼科医生像HRT和OCT这样的医学技术是昂贵且耗时的。因此,需要开发自动计算机辅助系统,其可以有效地且在更少的时间内能够有效地检测青光眼。光盘和光学杯是素材特征,有助于诊断青光眼。因此,光盘和光学杯的适当分割在检测疾病方面发挥着重要作用。在本文中,使用基于自适应阈值的基于图像质量和不变的噪声的方法,用于将光盘,光学杯,神经统计边缘和杯与盘比进行分段为筛选到筛选。另一种眼睛参数,RIM对盘比也考虑,其与CDR组合在确定青光眼并使系统更加坚固方面具有更多的可靠性。此外,已经使用SVM分类器来将图像分类为葡萄糖或非葡萄糖。将获得的实验结果与眼科医生的实验结果进行比较,发现高精度为90%。另外,所提出的方法具有低计算成本的速度更快。

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