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基于胸椎特征的个体识别方法的建立。

Development of individual identification method using thoracic vertebral features as biometric fingerprints.

机构信息

Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata, Niigata, 951-8518, Japan.

Division of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata, Niigata, Japan.

出版信息

Sci Rep. 2022 Sep 29;12(1):16274. doi: 10.1038/s41598-022-20748-w.

Abstract

Identification of individuals is performed when a corpse is found after a natural disaster, incident, or accident. DNA and dental records are frequently used as biometric fingerprints; however, identification may be difficult in some cases due to decomposition or damage to the corpse. The present study aimed to develop an individual identification method using thoracic vertebral features as a biological fingerprint. In this method, the shortest diameter in height, width, and depth of the thoracic vertebrae in the postmortem image and a control antemortem were recorded and a database was compiled using this information. The Euclidean distance or the modified Hausdorff distance was calculated as the distance between two points on the three-dimensional feature space of these measurement data. The thoracic vertebrae T1-12 were measured and the pair with the smallest distance was considered to be from the same person. The accuracy of this method for identifying individuals was evaluated by matching images of 82 cases from a total of 702 antemortem images and showed a hit ratio of 100%. Therefore, this method may be used to identify individuals with high accuracy.

摘要

当在自然灾害、事件或事故后发现尸体时,需要进行身份识别。DNA 和牙齿记录通常被用作生物特征指纹;然而,由于尸体的分解或损坏,在某些情况下可能难以识别。本研究旨在开发一种使用胸腰椎特征作为生物指纹的个体识别方法。在该方法中,记录死后图像和对照生前图像中胸腰椎的高度、宽度和深度的最短直径,并使用这些信息编制数据库。计算欧几里得距离或修正的 Hausdorff 距离作为这些测量数据的三维特征空间上两点之间的距离。测量 T1-12 胸椎,将距离最小的一对视为来自同一人。通过匹配总共 702 张生前图像中的 82 例图像,评估了该方法识别个体的准确性,准确率为 100%。因此,该方法可能用于高精度地识别个体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1096/9522789/f5b46f359618/41598_2022_20748_Fig1_HTML.jpg

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