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在视网膜图像中开发智能模型以检测糖尿病视网膜病变的程度

Fatemeh Darakhshan¹, Hamid Reza Naji¹

¹Department of Computer Engineering and Information Technology, Faculty of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran.

Journal of medical engineering & technology

|September 5, 2025

概括

这项研究通过在视网膜图像中识别微动脉瘤,为早期发现糖尿病视网膜病变提供了一种自动化方法. 这种方法实现了高精度,有助于及时治疗和维护视力.

科学领域:

眼科眼科
医学成像
计算机视觉

背景情况:

糖尿病视网膜病变是导致失明的主要原因,其特征是微动脉瘤作为早期指标.
早期发现小动脉瘤对于有效治疗和预防视力损失至关重要.

研究的目的:

开发和评估使用视网膜底图像准确检测微动脉瘤和糖尿病视网膜病变的自动化方法.
通过先进的图像处理和机器学习技术改善糖尿病视网膜病变的早期诊断.

主要方法:

图像预处理包括血管细分,圆边检测和光盘检测.
在去除不相关结构后使用圆形Hough转换检测微动脉瘤.
基于血管/微动脉瘤区域和灰色水平共发生矩阵 (GLCM) 的特征提取.
使用高斯核的支持矢量机 (SVM) 进行分类.

主要成果:

在EyePacs视网膜底部图像数据库中,该方法的准确性达到了95. 20%和特异性达到了97%.
与现有的糖尿病视网膜病变检测方法相比,实验结果显示出更高的性能.

结论:

开发的自动化系统能够有效地检测出小动脉瘤,从而能够及早诊断出糖尿病视网膜病变.
这种方法在糖尿病视网膜病变的查和治疗中具有显著的临床应用潜力,从而保护患者的视力.

关键词:

糖尿病视网膜病变在SVM 智能模型小动脉瘤形态上的视网膜图像

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