利用计算机辅助诊断系统诊断糖尿病视网膜病变

Hattan Omar Mujalled, Y. Kadah
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引用次数: 0

摘要

目的:糖尿病被认为是人类传播最广泛的疾病之一;失明被认为是最严重的后果。糖尿病会损害视网膜血管,对眼睛造成严重问题,最终可能导致失明。这种医学状况被称为“糖尿病视网膜病变”(DR)。在这种诊断中,视网膜微血管会经历几个阶段的变化威胁。在DR的早期阶段,检测视网膜血管的形成有助于预防疾病的危险影响。因此,制定一种早期诊断方法是有帮助的。因此,这项工作旨在开发一种检测和分类视网膜形成的系统,试图避免相关的影响。方法:目前的方法依赖于眼底相机从视网膜图像中提取血管边缘图。利用这种映射提取定量纹理特征。该系统测试了正常和异常图像中两组独立的感兴趣区域。这两组图像是从89张眼底图像的地面真值图像中提取出来的。眼底图像来自标准糖尿病视网膜病变数据库(DIARETDB1)的注释图像。结果:该系统的准确度为71%,灵敏度为75%。建议:当前的工作可能为未来的工作提供改进的机会。可以采用其他方法来提高系统的精度和灵敏度。此外,目前的系统可以在更大的样本量上进行测试,以研究这种影响。最后,当前方法的易用性使其在适当的诊断,特别是在早期阶段采用更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnosis of Diabetic Retinopathy Utilizing Computer-Aided Diagnosis System
Purpose: Diabetes is considered one of most diseases spread among people; blindness is considered the most resulted effect. Diabetes can damage the retinal blood vessels and cause severe problems to the eyes, which may end with sight loss. Such medical condition is known as "diabetic retinopathy" (DR). In such a diagnosis, the retinal microvascular go through several stages of change threat. In the early stages of the DR, detecting the formation that happened to the retinal blood vessels helps prevent the disease's dangerous effects. Therefore, producing a method to diagnose the disease in the early stages is helpful. So, this work aimed to develop a system of detecting and classifying the retina formation, trying to avoid relevant effects.Methodology: The current method depends on vascular edges map extracted from images of retina captured by a fundus camera. Such a map been utilized to extract quantitative texture features. The system tested two independent groups of the region of interest in normal and abnormal images. The two sets were extracted from ground truth images of the 89 fundus images. Fundus images were annotated images from the Standard Diabetic Retinopathy Database (DIARETDB1).Findings: The system provided an accuracy of 71% with a sensitivity of 75%.Recommendation: The current work may open an opportunity for improvement for future work. Other methods may be reached to raise the accuracy and the sensitivity of the system. Besides, the current system may be tested on a larger sample size to study such effects. Finally, the ease of the current method makes it faster in adoption in the appropriate diagnosis especially in the early stages.
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