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Forensic Identification from Three-Dimensional Sphenoid Sinus Images Using the Iterative Closest Point Algorithm

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Abstract

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.

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source point set and scene target set

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The data and material that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Xiaoai Dong, Fei Fan, Wei Wu, Kui Zhang, Ji Zhang & Zhenhua Deng

  2. Department of Computer Science, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Hanjie Wen & Hu Chen

Authors
  1. Xiaoai Dong
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  2. Fei Fan
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  3. Wei Wu
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  6. Kui Zhang
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  7. Ji Zhang
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  8. Zhenhua Deng
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Correspondence to Kui Zhang, Ji Zhang or Zhenhua Deng.

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The present study was a retrospective study with approval by the Ethics Committee of Sichuan University and a waiver of the requirement for informed consent was obtained.

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The authors declare no competing interests.

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Xiaoai Dong and Fei Fan contributed equally to this work.

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Dong, X., Fan, F., Wu, W. et al. Forensic Identification from Three-Dimensional Sphenoid Sinus Images Using the Iterative Closest Point Algorithm. J Digit Imaging 35, 1034–1040 (2022). https://doi.org/10.1007/s10278-021-00572-w

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  • DOI: https://doi.org/10.1007/s10278-021-00572-w

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