Hipwell J H, Strachan F, Olson J A, McHardy K C, Sharp P F, Forrester J V
Bio-medical Physics and Bio-engineering University of Aberdeen, Foresterhill, UK.
Diabet Med. 2000 Aug;17(8):588-94. doi: 10.1046/j.1464-5491.2000.00338.x.
To develop a technique to detect microaneurysms automatically in 50 degrees digital red-free fundus photographs and evaluate its performance as a tool for screening diabetic patients for retinopathy.
Candidate microaneurysms are extracted, after the image has been modified to remove variations in background intensity, by algorithms that enhance small round features. Each microaneurysm candidate is then classified according to its intensity and size by the application of a set of rules derived from a training set of 102 images.
When 3,783 individual images were analysed and the results compared with the opinion of a clinical research fellow examining the same images, the program achieved a sensitivity of 81% and a specificity of 93% for the detection of images containing microaneurysms. Nine hundred and twenty-five sets of 4 images per patient were then analysed and the total number of microaneurysms detected compared with the overall patient retinopathy grade derived by the clinician examining the same images. In this context, intended to mimic a screening situation, the program achieved a sensitivity of 85% and a specificity of 76% for the detection of patients with (any) retinopathy (positive predictive value 0.71, negative predictive value 0.88).
An automated technique was developed to detect retinopathy in digital red-free fundus images that can form part of a diabetic retinopathy screening programme. It is believed that it can perform a useful role in this context identifying images worthy of closer inspection or eliminating 50% or more of the screening population who have no retinopathy.
开发一种技术，用于在50度数码无赤眼底照片中自动检测微动脉瘤，并评估其作为筛查糖尿病患者视网膜病变工具的性能。
在对图像进行修改以消除背景强度变化后，通过增强小圆形特征的算法提取候选微动脉瘤。然后，根据从102幅图像的训练集中得出的一组规则，对每个微动脉瘤候选者按其强度和大小进行分类。
当分析3783幅个体图像并将结果与一位临床研究员对相同图像的判断进行比较时，该程序检测含有微动脉瘤图像的灵敏度为81%，特异性为93%。然后分析了925组每位患者4幅图像，并将检测到的微动脉瘤总数与检查相同图像的临床医生得出的患者总体视网膜病变分级进行比较。在这种旨在模拟筛查情况的背景下，该程序检测患有（任何）视网膜病变患者的灵敏度为85%，特异性为76%（阳性预测值0.71，阴性预测值0.88）。
开发了一种自动技术，用于在数码无赤眼底图像中检测视网膜病变，该技术可成为糖尿病视网膜病变筛查程序的一部分。据信，它在这种情况下可发挥有益作用，识别值得进一步检查的图像，或排除50%或更多无视网膜病变的筛查人群。
