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一种基于灰色关系分析的单样视网膜血管细分方法.

Yating Wang¹, Hongjun Li¹

¹School of Information Science and Technology, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)

|July 13, 2024

概括

本研究引入了一种新的灰色关系分析方法,用于准确的视网膜血管细分,解决有限的数据挑战. 这种方法显著提高了眼睛疾病的诊断能力.

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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:

准确的视网膜血管细分对于诊断和治疗各种眼睛疾病至关重要.
有限的注释数据集对开发强大的细分模型构成重大挑战.
灰色理论为处理数据稀缺和信息差的问题提供了一个有前途的方法.

研究的目的:

提出一种基于灰色关系的新方法,用于准确的视网膜血管细分.
解决医疗图像分析中小型和未标记数据集的局限性.
加强计算机辅助诊断系统用于视网膜疾病.

主要方法:

开发了一种基于灰色关系分析 (NADF-GRA) 的噪声适应性歧视过算法,用于图像增强.
设计了一个基于灰色关系分析 (TS-GRA) 的值细分模型,用于细分增强的船舶图像.
实现了后处理阶段,包括孔填充和孤立像素去除,以进行精细的细分.

主要成果:

拟议的方法在公共数据集 (DRIVE,STARE,HRF) 上显示出高性能.
在DRIVE数据集上获得了96.03%的平均准确性和98.51%的特异性.
与当前主流的视网膜血管细分方法相比,它展示了优越的性能.

结论:

新的基于灰色关系的方法为视网膜血管细分提供了有效的解决方案,特别是在有限的数据的情况下.
该方法提供了准确和强大的细分结果,优于现有技术.
这种方法有可能在眼科中显著推进计算机辅助诊断.

关键词:

适应性歧视性过方式灰色的关系分析.视网膜血管细分器的细分没有监督的算法.

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