关于使用全景射线图和计算机断层扫描进行自动个人识别的审查
1Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany.
RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
|March 4, 2026
概括
放射学可以在医学图像中使用独特的解剖特征来实现未知个体的自动识别. 基于描述器的计算机视觉对于大型数据库来说是强大的,而深度学习则显示出有希望的结果,但需要进一步验证.
科学领域:
- 法医放射学 法医放射学
- 医学成像分析 医学成像分析
- 生物识别信息 生物识别信息
背景情况:
- 识别未知的个人是法医和紧急医学的关键挑战.
- 放射图像提供了独特的解剖特征,用于个人识别.
- 目前基于放射学识别的应用在很大程度上是实验性的.
研究的目的:
- 审查目前基于放射学的自动个人识别方法.
- 评估这些识别方法的性能.
- 突出在法医和临床环境中的潜在应用.
主要方法:
- 对从2018年起发表的研究进行了叙述性审查.
- 使用PubMed和谷歌学者进行文献搜索.
- 包括使用全景放射 (PR) 或计算机断层扫描 (CT) 进行自动或半自动识别的研究.
主要成果:
- 包括32项研究,重点关注PR-to-PR,CT-to-CT和CT-to-PR识别.
- 基于描述器的计算机视觉是最常见的方法 (9项研究).
- 深度学习方法在特征提取,分类和骨细分方面表现有前途.
结论:
- 基于描述器的计算机视觉对于大数据库比较和死后识别来说是灵活和强大的.
- 深度学习方法需要进一步验证跨个体匹配.
- 标准化数据库和自动化管道对于推进基于放射学识别至关重要.
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