West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.
Department of Computer Science, Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.
J Digit Imaging. 2022 Aug;35(4):1034-1040. doi: 10.1007/s10278-021-00572-w. Epub 2022 Apr 4.
Forensic identification of human remains is crucial for legal, humanitarian, and civil reasons. Wide heterogeneity in sphenoid sinus morphology can be used for personal identification. This study aimed to propose a new protocol for personal identification based on three-dimensional (3D) reconstruction of sphenoid sinus CT images using Iterative Closest Point (ICP) algorithm. Seven hundred thirty-two patients which consisted of 348 females and 384 males were retrospectively included. The study sample includes 732 previous images as a source point set and 743 later ones as a scene target set. The sphenoid sinus computed tomography (CT) images were processed on a workstation (Dolphin imaging) to obtain 3D images and stored as a file format of Stereo lithography (.STL). Then, a Python library vtkplotter was used to transform the STL format to PLY format, which was adapted to Point Cloud Library (PCL). The ICP algorithm was used for point clouds matching. The metric Rank-N recognition rate was used for evaluation. The scene target set of 743 individuals was compared with the source point set of 732 individual models and achieved Rank-1 accuracy of 96.24%, Rank-2 accuracy of 99.73%, and Rank-3 accuracy of 100%. Our results indicated that the 3D point cloud registration of sphenoid sinuses was useful for assessing personal identification in forensic contexts.
法医学中对人类遗骸的鉴定具有重要的法律、人道和民事意义。蝶窦形态的广泛异质性可用于个人识别。本研究旨在提出一种新的基于迭代最近点（ICP）算法的蝶窦 CT 图像三维重建的个人识别方案。共回顾性纳入 732 例患者，其中女性 348 例，男性 384 例。研究样本包括 732 例原始图像作为源点集和 743 例后期图像作为场景目标集。蝶窦 CT 图像在工作站（Dolphin imaging）上进行处理，以获得三维图像并存储为 Stereo lithography（.STL）文件格式。然后，使用 Python 库 vtkplotter 将 STL 格式转换为 PLY 格式，再适应点云库（PCL）。使用 ICP 算法进行点云匹配。使用度量 Rank-N 识别率进行评估。将 743 个人的场景目标集与 732 个个体模型的源点集进行比较，获得了 96.24%的 Rank-1 准确率、99.73%的 Rank-2 准确率和 100%的 Rank-3 准确率。我们的结果表明，蝶窦的三维点云配准在法医学背景下评估个人识别是有用的。
