目标:计算机视觉(CV)模仿人类视觉,使计算机能够自动将最近检查的放射图像与大型图像数据库进行比较,以进行唯一识别,在涉及未知患者或死者的紧急情况下至关重要。本研究旨在使用单个CT切片将基于CV的个人识别方法从正交图(OPG)扩展到计算机断层扫描(CT)检查。
方法:该研究分析了722名患者的819次头颅计算机断层扫描(CCT)检查,专注于来自六个解剖区域的单个CT切片,以探索它们在69例手术中基于CV的个人识别的潜力。CV自动识别和描述图像中的有趣特征,可以在参考图像中识别,然后指定为匹配点。在这项研究中,匹配点的数量被用作识别的指标.
结果:在六个不同地区,在700多个可能的身份中,识别率从41/69(59%)到69/69(100%)不等。来自同一个人的图像比较获得了更高的匹配点,平均6.32±0.52%(100%代表最大可能匹配点),而不同个体的图像平均为0.94±0.15%。在牙齿中找到可靠的匹配点,上颌骨,颈椎,头骨,和鼻旁窦,上颌窦和筛细胞由于其丰富的匹配点而特别适合鉴定。
结论:基于单个CT切片可以实现对个体的明确识别,上颌窦CT切片的识别率最高。然而,金属制品,尤其是牙齿修复术,和各种头部位置可以阻碍识别。
结论:放射学拥有许多CV数据库的参考图像,在紧急检查或涉及未知死者的情况下,促进基于CV的自动个人身份识别。这通过允许访问病史来增强患者护理和与亲属的沟通。
结论:放射学或法医学领域的未知个体构成了挑战,通过自动基于CV的识别方法解决。突出显示上颌窦的单个CT切片对于个人识别特别有效。放射学通过利用其广泛的图像数据库在自动个人识别中起着关键作用。
OBJECTIVE: Computer vision (CV) mimics human vision, enabling computers to automatically compare radiological images from recent examinations with a large image database for unique identification, crucial in emergency scenarios involving unknown patients or deceased individuals. This study aims to extend a CV-based personal identification method from orthopantomograms (OPGs) to computed tomography (CT) examinations using single CT slices.
METHODS: The study analyzed 819 cranial computed tomography (CCT) examinations from 722 individuals, focusing on single CT slices from six anatomical regions to explore their potential for CV-based personal identification in 69 procedures. CV automatically identifies and describes interesting features in images, which can be recognized in a reference image and then designated as matching points. In this study, the number of matching points was used as an indicator for identification.
RESULTS: Across six different regions, identification rates ranged from 41/69 (59%) to 69/69 (100%) across over 700 possible identities. Comparison of images from the same individual achieved higher matching points, averaging 6.32 ± 0.52% (100% represents the maximum possible matching points), while images of different individuals averaged 0.94 ± 0.15%. Reliable matching points are found in the teeth, maxilla, cervical spine, skull bones, and paranasal sinuses, with the maxillary sinuses and ethmoidal cells being particularly suitable for identification due to their abundant matching points.
CONCLUSIONS: Unambiguous identification of individuals based on a single CT slice is achievable, with maxillary sinus CT slices showing the highest identification rates. However, metal artifacts, especially from dental prosthetics, and various head positions can hinder identification.
CONCLUSIONS: Radiology possesses a multitude of reference images for a CV database, facilitating automated CV-based personal identification in emergency examinations or cases involving unknown deceased individuals. This enhances patient care and communication with relatives by granting access to medical history.
CONCLUSIONS: Unknown individuals in radiology or forensics pose challenges, addressed through automatic CV-based identification methods. A single CT slice highlighting the maxillary sinuses is particularly effective for personal identification. Radiology plays a pivotal role in automated personal identification by leveraging its extensive image database.