基于特征提取和V-描述器的COVID-19严重性评估
Ben Ye1, Xixi Yuan1, Zhanchuan Cai1
1Faculty of Information TechnologyMacau University of Science and Technology Macau 999078 China.
概括
这项研究引入了一种使用胸部CT扫描评估冠状病毒病 (COVID-19) 严重程度的新方法. 该技术识别了肺部感染特征,以更准确地估计COVID-19的严重程度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 数字图像特征识别对于包括医疗诊断在内的工业应用至关重要.
- 准确评估冠状病毒病 (COVID-19) 严重程度对于患者管理至关重要.
研究的目的:
- 提出一种新的方法来识别胸部CT图像中肺部感染的丰富特征.
- 根据提取的图像特征开发一种有效的技术,以评估COVID-19的严重程度.
主要方法:
- 从胸部CT图像中对双侧肺部进行细分.
- 从受感染的肺部区域提取纹理特征 (粗度,对比度,粗度,度).
- 纹理特征和V-描述器的融合用于COVID-19严重程度估计.
主要成果:
- 在COVID-19CT图像上演示特征提取和肺病变细分.
- 与其他方法相比,V-描述器显示出有效的损伤轮重建.
- 建议的评估描述符准确地反映了感染率和病变密度.
结论:
- 开发的方法通过分析CT扫描中的肺部感染特征来准确估计COVID-19的严重程度.
- 融合特征描述器为评估疾病严重程度提供了可靠的衡量标准.
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