Department of Ophthalmology, Faculty of Medicine, Medical University of Bialystok, 24A Curie-Sklodowskiej Street, 15-276 Bialystok, Poland.
Bialystok University of Technology, Faculty of Computer Science, 45A Wiejska Street,15-351 Białystok, Poland.
Sensors (Basel). 2019 Feb 8;19(3):695. doi: 10.3390/s19030695.
Hard exudates are one of the most characteristic and dangerous signs of diabetic retinopathy. They can be marked during the routine ophthalmological examination and seen in color fundus photographs (i.e., using a fundus camera). The purpose of this paper is to introduce an algorithm that can extract pathological changes (i.e., hard exudates) in diabetic retinopathy. This was a retrospective, nonrandomized study. A total of 100 photos were included in the analysis-50 sick and 50 normal eyes. Small lesions in diabetic retinopathy could be automatically diagnosed by the system with an accuracy of 98%. During the experiments, the authors used classical image processing methods such as binarization or median filtration, and data was read from the d-Eye sensor. Sixty-seven patients (39 females and 28 males with ages ranging between 50 and 64) were examined. The results have shown that the proposed solution accuracy level equals 98%. Moreover, the algorithm returns correct classification decisions for high quality images and low quality samples. Furthermore, we consider taking retina photos using mobile phones rather than fundus cameras, which is more practical. The paper presents an innovative approach. The results are introduced and the algorithm is described.
硬性渗出物是糖尿病视网膜病变最具特征性和危险性的标志之一。它们可以在常规眼科检查中被标记，并在眼底彩色照片中看到（即使用眼底相机）。本文的目的是介绍一种可以提取糖尿病性视网膜病变中病理性改变（即硬性渗出物）的算法。这是一项回顾性、非随机研究。分析中总共包括 100 张照片，其中 50 张是患病眼睛的照片，50 张是正常眼睛的照片。该系统可以自动诊断糖尿病视网膜病变中的小病变，准确率达到 98%。在实验过程中，作者使用了经典的图像处理方法，如二值化或中值滤波，并从 d-Eye 传感器读取数据。共检查了 67 名患者（39 名女性和 28 名男性，年龄在 50 至 64 岁之间）。结果表明，所提出的解决方案的准确率达到 98%。此外，该算法对高质量图像和低质量样本都能返回正确的分类决策。此外，我们还考虑使用手机而不是眼底相机拍摄视网膜照片，这更实用。本文提出了一种创新的方法。介绍了结果并描述了算法。
