Automated identification of diabetic retinopathy stages using digital fundus images
Automated identification of diabetic retinopathy stages using digital fundus images
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DOI：
10.1007/s10916-007-9113-9
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发表时间：
2008-04-01
影响因子：
5.3
通讯作者：
Kagathi, Manjunath
中科院分区：
文献类型：
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作者：
Nayak, Jagadish;Bhat, P. Subbanna;Kagathi, Manjunath
糖尿病视网膜病变（DR）是由糖尿病患者眼睛后部视网膜的小血管损伤引起的。糖尿病视网膜病变的主要阶段是非增殖性糖尿病视网膜病变（NPDR）和增殖性糖尿病视网膜病变（PDR）。眼底照片在临床上广泛应用于各种眼科疾病的诊断和治疗。它也是糖尿病视网膜病变大规模筛查的主要资源之一。在这项工作中，我们提出了一种基于计算机的方法，使用眼底图像检测糖尿病视网膜病变阶段。通过对眼底图像进行预处理、形态学处理和纹理分析等方法，检测出硬性渗出物面积、血管面积和对比度等特征。我们的方案共使用了140名受试者，包括两个阶段的DR和正常。我们提取的特征具有统计学显著性（p < 0.0001），具有不同的平均值+/- SD，如表1所示。然后，这些特征被用作人工神经网络（ANN）的输入，用于自动分类。通过与眼科专家的对比，验证了检测结果。我们证明了93%的分类准确性，90%的敏感性和100%的特异性。
Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources for mass screening of diabetic retinopathy. In this work, we have proposed a computer-based approach for the detection of diabetic retinopathy stage using fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of hard exudates, area of the blood vessels and the contrast. Our protocol uses total of 140 subjects consisting of two stages of DR and normal. Our extracted features are statistically significant (p < 0.0001) with distinct mean +/- SD as shown in Table 1. These features are then used as an input to the artificial neural network (ANN) for an automatic classification. The detection results are validated by comparing it with expert ophthalmologists. We demonstrated a classification accuracy of 93%, sensitivity of 90% and specificity of 100%.