Department of Information Technology, Sirindhorn International Institute of Technology, Thammasat University 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani 12000, Thailand; E-mails:
Sensors (Basel). 2009;9(3):2148-61. doi: 10.3390/s90302148. Epub 2009 Mar 24.
Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.
渗出物是糖尿病视网膜病变的主要征象。早期发现可能降低失明的风险。本文提出了一种使用模糊 C 均值（FCM）聚类从非散瞳的糖尿病视网膜病变患者的低对比度数字图像中自动检测渗出物的方法。在提取四个特征（强度、强度标准差、色调和边缘像素数量）作为粗分割的输入参数之前，应用对比度增强预处理。然后使用形态学技术对第一个结果进行微调。通过与专家眼科医生的手绘地面实况进行比较来验证检测结果。使用灵敏度、特异性、阳性预测值（PPV）、阳性似然比（PLR）和准确率来评估整体性能。结果表明，该方法能够成功检测渗出物，其灵敏度、特异性、PPV、PLR 和准确率分别为 87.28%、99.24%、42.77%、224.26%和 99.11%。
