根据静脉检测器的发展,对静脉抽取图像的图像处理进行研究
So-Hyeon Bang1, Seung-Hun Kim2, Jin-Hyoung Jeong3
1Department of Biomedical Engineering, Catholic Kwandong University, Gangneung, Korea.
概括
这项研究开发了一种低成本的近红外 (NIR) 静脉检测系统,使用Raspberry Pi 4. 不清晰的掩盖和中间选算法有效地提高了静脉的可见性,改善了具有挑战性的患者群体的血管访问.
科学领域:
- 生物医学工程 生物医学工程
- 医学成像技术 医学成像技术
背景情况:
- 内静脉 (IV) 接入对于肥胖,老龄化或皮肤黑的患者来说具有挑战性.
- 低成本的近红外 (NIR) 静脉检测为改善血管通道提供了一个有希望的解决方案.
- 以前的研究通常依赖于高端设备或单个算法评估.
研究的目的:
- 开发一种负担得起的静脉检测系统,使用850nm NIR LED和Raspberry Pi 4.
- 评估和比较用于静脉检测的各种图像增强算法的性能.
- 通过使用诸如平均平方误差 (MSE),峰值信号对噪声比 (PSNR) 和结构相似性指数测量 (SSIM) 等量化指标来评估系统性能.
主要方法:
- 一个低成本的设备是用NIR LED模块,IR敏感摄像头和Raspberry Pi 4构建的.
- 应用了图像增强技术,包括对比度有限的自适应基因图平衡 (CLAHE),不清晰的掩饰,中位过器和模糊的自适应马.
- 对13名受试者的增强图像进行了量化评估,使用MSE,PSNR和SSIM进行了量化评估.
主要成果:
- 不清晰的掩饰显示出优异的对比度增强,MSE值最低 (36.17) 和PSNR值最高 (32.98).
- 中位过在保持结构完整方面表现出色,获得最高的SSIM (0.926).
- 一种组合方法 (CLAHE + Unsharp Masking + Median Filter) 显示出强大的整体性能,但由于过度增强和硬件限制,导致SSIM的轻微减少.
结论:
- 开发的低成本NIR系统有效地增强了血管图像,以改善静脉检测.
- 像Unsharp Masking和Median Filtering这样的特定算法显著改善了对比度和结构一致性.
- 未来的研究应该解决实时处理优化和硬件升级,以提高临床效用.
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