Department of Electronics and Communication Engineering, Punjab Engineering College (Deemed to be University), Sector 12, Chandigarh 160012, India.
Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India.
J Healthc Eng. 2023 Jul 14;2023:4537253. doi: 10.1155/2023/4537253. eCollection 2023.
Exudate, an asymptomatic yellow deposit on retina, is among the primary characteristics of background diabetic retinopathy. Background diabetic retinopathy is a retinopathy related to high blood sugar levels which slowly affects all the organs of the body. The early detection of exudates aids doctors in screening the patients suffering from background diabetic retinopathy. A computer-aided method proposed in the present work detects and then segments the exudates in the images of retina acquired using a digital fundus camera by (i) gradient method to trace the contour of exudates, (ii) marking the connected candidate pixels to remove false exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. The method is tested on 1307 retinal fundus images with varying characteristics. Six hundred and forty-nine images were acquired from hospital and the remaining 658 from open-source benchmark databases, namely, STARE, DRIVE MESSIDOR, DiaretDB1, and e-Ophtha. The exudates segmentation method proposed in this research work results in the retinal fundus image-based (i) accuracy of 98.04%, (ii) sensitivity of 95.345%, and (iii) specificity of 98.63%. The segmentation results for a number of exudates-based evaluations depict the average (i) accuracy of 95.68%, (ii) sensitivity of 93.44%, and (iii) specificity of 97.22%. The substantial combined performance at image and exudates-based evaluations proves the contribution of the proposed method in mass screening as well as treatment process of background diabetic retinopathy.
渗出物是视网膜上无症状的黄色沉积物，是背景性糖尿病性视网膜病变的主要特征之一。背景性糖尿病性视网膜病变是一种与高血糖水平相关的视网膜病变，它会缓慢影响身体的所有器官。渗出物的早期检测有助于医生对患有背景性糖尿病性视网膜病变的患者进行筛查。本工作中提出的一种计算机辅助方法通过（i）梯度方法来跟踪渗出物的轮廓，（ii）标记连接的候选像素以去除假渗出物像素，以及（iii）连接边缘像素来提取渗出物的边界，从而检测并分割数字眼底相机获取的视网膜图像中的渗出物。该方法在具有不同特征的 1307 张视网膜眼底图像上进行了测试。其中 649 张图像来自医院，其余 658 张图像来自开源基准数据库，即 STARE、DRIVE MESSIDOR、DiaretDB1 和 e-Ophtha。本研究工作中提出的渗出物分割方法在基于视网膜眼底图像的情况下的结果为（i）准确率为 98.04%，（ii）敏感度为 95.345%，以及（iii）特异性为 98.63%。对一些基于渗出物的评估的分割结果显示平均（i）准确率为 95.68%，（ii）敏感度为 93.44%，以及（iii）特异性为 97.22%。基于图像和基于渗出物的评估的综合性能表明，该方法在背景性糖尿病性视网膜病变的大规模筛查和治疗过程中具有重要作用。
