Department of Electronic, Computer Science and Automatic Engineering, University of Huelva, Huelva, Spain.
Escuela Técnica Superior de Ingeniería, Campus de La Rábida, Universidad de Huelva, Carretera Palos de La Frontera s/n, 21819, Palos de La Frontera, Huelva, Spain.
Med Biol Eng Comput. 2018 Aug;56(8):1379-1390. doi: 10.1007/s11517-017-1771-2. Epub 2018 Jan 10.
The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme. Graphical Abstract Diagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images.
本文旨在介绍一种眼底图像糖尿病性黄斑水肿（DME）风险检测系统的方法和初步结果。该系统基于视网膜渗出物（Ex）的检测，其在图像中的存在可用于疾病的早期诊断。为此，该系统将数字图像处理算法应用于视网膜图像，以获得一组候选区域作为 Ex，然后通过特征提取和监督分类技术对其进行验证。该系统对 529 名有 DME 风险的糖尿病患者的 1058 张视网膜照片进行了诊断，结果表明，该系统可以达到与眼科专家相当的灵敏度水平：它对每位患者的灵敏度为 0.9000，而几位专家的灵敏度分别为 0.7733、0.9133 和 0.9000，其中假阴性是疾病的轻度临床病例。此外，该系统的特异性达到 0.6939，足以筛选出约 70%没有 DME 证据的患者。这些值表明该系统符合其可能集成到完整的糖尿病性视网膜病变预筛查工具中的要求，以便在筛查计划中自动管理患者。 基于眼底图像中渗出物（Ex）检测的糖尿病性黄斑水肿（DME）风险诊断系统。
