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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Triangular geometry-based novel feature and a novel algorithm for human identification using dental radiographs.

Jincy Raju¹, Chintan K Modi

¹Electronics and Communication Engineering, Babaria Institute of Technology, Varnama, Vadodara, Gujarat, India. jincy_ec@yahoo.co.in

Journal of Medical Engineering & Technology

|June 19, 2013

Summary

This summary is machine-generated.

Dental X-rays are crucial for identifying unidentified individuals when other methods fail. A new tooth taper parameter improves matching of post-mortem (PM) and antemortem (AM) dental radiographs for reliable identification.

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Area of Science:

Forensic Dentistry
Biometrics
Image Processing

Background:

Dental radiography is vital for identifying missing or unidentified persons.
Dental features are stable over time, making them reliable for identification.
Existing methods rely on matching post-mortem (PM) and antemortem (AM) radiographs.

Purpose of the Study:

To develop an automated method for matching PM and AM dental radiographs.
To introduce a novel feature for enhanced dental identification accuracy.
To improve the reliability of forensic identification using dental imaging.

Main Methods:

Image enhancement and region of interest segmentation from dental radiographs.
Extraction of unique tooth features, including a new triangular geometry-based feature (tooth taper parameter).
Matching of feature vectors from PM images with AM images in a database.

Main Results:

A new tooth taper parameter based on triangular geometry was proposed.
The proposed feature facilitates accurate matching between PM and AM dental images.
Successful identification of subjects based on the novel feature matching.

Conclusions:

The tooth taper parameter offers a robust method for dental identification.
This approach enhances the accuracy and efficiency of forensic identification using dental X-rays.
Automated dental image analysis can significantly aid in identifying unidentified individuals.