ArticleJournal of digital imaging2022
Forensic Identification from Three-Dimensional Sphenoid Sinus Images Using the Iterative Closest Point Algorithm.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Humanitarian forensic medicine: a systematic review.International journal of legal medicine · 2025Pooled it
- Multi-View Elliptic Fourier Analysis of the Sternum for Automated Postmortem-to-Antemortem Computed Tomography Candidate Ranking.Journal of imaging informatics in medicine · 2026Article
- CBCT assessment of maxillary sinus sexual dimorphism: morphometry and sex-classification performance in Peruvian adults.The Journal of forensic odonto-stomatology · 2026Observational
- Automatic Personal Identification Using a Single MRI Slice.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative analysis of 3D reconstruction of paranasal sinuses for simulated personal identification using Dolphin imaging and mimics software.International journal of legal medicine · 2026Article
- Automated identification of unknown decedents: matching postmortem CT images with clinical databases.International journal of legal medicine · 2025Article
- Automatic personal identification using a single CT image.European radiology · 2025Article
- Identification score for robust and secure identification using ante- and post-mortem skull CT scans.International journal of legal medicine · 2025Article
- Development of individual identification method using thoracic vertebral features as biometric fingerprints.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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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