确定肺部受冠状病毒影响的区域 计算机断层扫描图像通过三相水平设置和雪莱特转换
Nasser Aghazadeh1,2,3, Parisa Noras1, Sevda Moghaddasighamchi1,4
1Department of Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran.
Journal of medical signals and sensors
|December 18, 2025
概括
这项研究引入了一种新的多相细分方法,使用shearlet和相延伸变换来准确识别COVID-19肺部感染. 该技术精确量化了肺部的参与,并提高了诊断能力.
科学领域:
- 医疗成像医学成像
- 计算病理学计算病理学
背景情况:
- 随着COVID-19的流行,导致严重的呼吸道并发症,使肺部参与成为一个关键的诊断因素.
- 精确细分受感染的肺部区域对于有效的COVID-19诊断和治疗至关重要.
研究的目的:
- 开发和评估一种多相细分方法,以精确识别COVID-19感染的肺部区域.
- 提高COVID-19患者肺部参与量的定量评估的准确性和效率.
主要方法:
- 一种基于水平设置框架的多相细分方法.
- 整合shearlet变换来引导梯度流,用于高精度的定向多分辨率分析.
- 应用相延伸变换 (PST) 来增强对比度和提高分段收速度.
主要成果:
- 拟议的算法在500张测试图像中证明了感染的肺部区域的准确识别.
- 细分精度使得肺部参与率的精确计算成为可能.
- 使用shearlet变换实现了地面玻璃不透明边界的清晰划分.
结论:
- 增强的多阶段级设置方法有效地细分了COVID-19感染的肺部区域.
- 剪刀和相延伸变换的组合提高了细分精度和计算效率.
- 这种方法为定量评估COVID-19肺部参与提供了可靠的工具.
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