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A novel method for glaucoma detection using optic disc and cup segmentation in digital retinal fundus images

机译:视盘和杯分割在视网膜数字化眼底图像中检测青光眼的新方法

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Retinal fundus photographs has always remained the gold standard for evaluating the changes in retina. Here, a novel method for automatic glaucoma detection from digital retinal fundus images is proposed. The methodology makes use of optic disc and cup segmentation. Optic disc is segmented using morphological operations and hybrid level-set methodology. Optic cup is segmented by first detecting blood vessels using SVM classifier and then the bending points on the circum linear vessels. Parameters such as vertical cup-to-disc ratio (CDR), cup-to-disc area ratio are calculated and used for glaucoma detection. A CDR value greater than 0.5 and cup-to-disc area ratio greater than 0.3 indicates the presence of glaucoma. The proposed method is found to produce a mean error as low as 0.021 (CDR) when compared with expert observation.
机译:视网膜底照片一直是评估视网膜变化的金标准。在此,提出了一种从视网膜数字化眼底图像自动检测青光眼的新方法。该方法利用了视盘和杯的分割。使用形态学操作和混合水平集方法对光盘进行分段。通过首先使用SVM分类器检测血管,然后在外接线性血管上弯曲点来对视杯进行分割。计算诸如垂直杯碟比(CDR),杯碟比之类的参数,并将其用于青光眼检测。 CDR值大于0.5且杯碟面积比大于0.3表示存在青光眼。与专家观察相比,发现该方法产生的平均误差低至0.021(CDR)。

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