Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education ( Deemed to be University), Srivilliputtur, Tamilnadu, India.
Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, India.
J Med Syst. 2019 May 8;43(6):173. doi: 10.1007/s10916-019-1313-6.
Diabetes is characterized by constant high level of blood glucose. The human body needs to maintain insulin at very constrict range. The patients who are all affected by diabetes for a long time affected by eye disease called Diabetic Retinopathy (DR). The retinal landmarks namely Optic disc is predicted and masked to decrease the false positive in the exudates detection. The abnormalities like Exudates, Microaneurysms and Hemorrhages are segmented to classify the various stages of DR. The proposed approach is employed to separate the landmarks of retina and lesions of retina for the classification of stages of DR. The segmentation algorithms like Gabor double-sided hysteresis thresholding, maximum intensity variation, inverse surface adaptive thresholding, multi-agent approach and toboggan segmentation are used to detect and segment BVs, ODs, EXs, MAs and HAs. The feature vector formation and machine learning algorithm used to classify the various stages of DR are evaluated using images available in various retinal databases, and their performance measures are presented in this paper.
糖尿病的特征是血糖水平持续偏高。人体需要将胰岛素维持在非常严格的范围内。长期受糖尿病影响的患者会患上一种称为糖尿病性视网膜病变（DR）的眼部疾病。预测并屏蔽视网膜地标（即视盘），以减少渗出物检测中的假阳性。对异常情况（如渗出物、微动脉瘤和出血）进行分割，以对 DR 的各个阶段进行分类。所提出的方法用于分离视网膜的地标和视网膜的病变，以对 DR 的阶段进行分类。使用分割算法（如双边谷值 hysteresis 阈值、最大强度变化、反向曲面自适应阈值、多代理方法和 toboggan 分割）来检测和分割 BVs、ODs、EXs、MAs 和 HAs。使用在各种视网膜数据库中可用的图像来评估特征向量形成和机器学习算法，以对 DR 的各个阶段进行分类，并在本文中呈现其性能指标。
