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自动光盘细分用于光盘的分类,使用因子化梯度向量流量
Seint Lei Naing1, Pakinee Aimmanee2
1School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tiwanon Rd, Bangkadi, Meung, Patumthani, 12000, Thailand.
Scientific reports
|January 3, 2024
概括
早期检测光盘 (ODE) 对于预防视力丧失至关重要. 这项研究引入了一种自动化方法,用于在视网膜图像中分类 edematous 和非 edematous 光学盘,实现高精度.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 光盘 (ODE) 是神经眼科疾病的关键眼部症状.
- 早期发现ODE对于预防视力损失至关重要.
- 由于纹理差异,标准图像分析技术可能会在 edematous 光学盘上失败.
研究的目的:
- 开发一个完全自动化的算法来将光盘分类为 edematous 或非 edematous.
- 提高 fundus 图像中 ODE 检测的准确性和效率.
主要方法:
- 拟议的算法包括自动光盘定位,使用因子化梯度向量流 (FGVF) 的细分和分类.
- 使用线性支向量机 (SVM) 分类器,利用来自容器的27个特征,GLCM,颜色和强度配置文件.
- 该方法在来自三个数据集的295张 fundus 图像上得到了验证.
主要成果:
- 分段组件的平均精度为88.41%,回忆率为89.35%,F1-Score为86.53%.
- 自动分类系统的平均准确率高达99.40%.
- 拟议的方法比现有的最先进的技术效率高出3.43%.
结论:
- 开发的自动化系统有效地分类 edematous 和非 edematous 光学磁盘.
- 这种方法显示了光盘的早期和准确诊断的巨大潜力.
- 高精度表明在神经眼科和视网膜成像分析中具有临床实用性.
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