使用值估计方法对具有不同复杂性的医疗噪音图像进行边缘检测操作员的比较分析
Vladimir Maksimovic1, Branimir Jaksic1, Mirko Milosevic2
1Faculty of Technical Sciences, University of Pristina in Kosovska Mitrovica, Kneza Milosa 7, 38220 Kosovska Mitrovica, Serbia.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了一种新的值估计方法,以改善噪音严重的医疗图像中的边缘检测,提高诊断准确度. 该方法有效地减少了各种图像复杂性和数据集的噪声影响.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 计算机视觉 计算机视觉
背景情况:
- 噪音显著降低了医疗图像质量,可能导致误诊.
- 精确的边缘检测对于分析医学扫描中的关键特征至关重要.
- 现有的边缘检测方法在复杂的医学图像中与噪声作斗争.
研究的目的:
- 建议和评估一种新的值估计方法,用于在杂的医疗图像中进行强大的边缘检测.
- 评估噪声对边缘检测在不同图像复杂度和医疗数据集中的影响.
- 将拟议的方法与传统和基于深度学习的边缘检测技术进行比较.
主要方法:
- 开发了一种新的值估计方法,并应用于边缘检测算法.
- 该方法在医疗图像 (视网膜,脑瘤,肺部CT) 上进行了测试,噪音类型和水平各不相同.
- 使用了网格搜索 (GS) 和随机搜索 (RS9) 优化方法.
- 使用低,中,高细分级别图像的指标来评估性能.
- 与诸如AlexNet,ResNet,VGGNet,MobileNetv2和Inceptionv3.3之类的深度学习模型进行了比较.
主要成果:
- 提出的值估计方法显著提高了边缘检测性能,特别是与Canny操作员,在各种噪声条件下.
- 拉普拉斯操作员对噪音高度敏感,在使用新方法,特别是使用网格搜索时,表现出明显的改善.
- 该方法在视网膜,脑瘤和肺部CT数据集的复杂医学图像上表现出有效性.
- 对比分析显示了与领先的深度学习边缘检测技术相比具有竞争力或优异的性能.
结论:
- 拟议的值估计方法提供了一个强大的解决方案,用于增强噪音医疗图像中的边缘检测.
- 这种技术对于提高医学图像分析的可靠性和减少诊断错误至关重要.
- 该研究强调了噪声强大的算法在医学成像临床应用中的重要性.
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