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Gray level co-occurrence and random forest algorithm-based gender determination with maxillary tooth plaster images.

Betül Akkoç1, Ahmet Arslan2, Hatice Kök3

  • 1Department of Computer Engineering, Selçuk University, Konya, Turkey.

Computers in Biology and Medicine
|April 23, 2016
PubMed
Summary

This study shows that gender can be determined from maxillary tooth plaster models with 90% accuracy using a Random Forest algorithm. This method offers a reliable forensic tool for identifying gender in disaster victim identification.

Keywords:
Feature extractionGender determinationImage processingRandom forest algorithm

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Area of Science:

  • Forensic Odontology
  • Biometrics
  • Computer Vision

Background:

  • Gender is a key identifier, and dental structures are crucial for identification in forensic cases.
  • Existing methods for gender determination from teeth can be time-consuming or require specialized expertise.
  • Developing automated, accurate methods for gender determination from dental records is essential for forensic science.

Purpose of the Study:

  • To develop and evaluate an automated system for gender determination using maxillary tooth plaster models.
  • To assess the accuracy of a Random Forest algorithm in classifying gender based on dental features.
  • To establish the potential of dental imagery analysis in forensic identification.

Main Methods:

  • Maxillary tooth plaster models from 40 individuals (20 males, 20 females) were analyzed.
  • Images of the models were captured using a standardized lighting setup.
  • Features were extracted from gray level co-occurrence matrices of segmented images and classified using a Random Forest algorithm.

Main Results:

  • The automated system achieved a 90% success rate in gender determination.
  • The Random Forest algorithm effectively classified gender based on extracted dental features.
  • The study demonstrated the feasibility of using dental plaster models for automated gender identification.

Conclusions:

  • Gender determination from maxillary tooth plaster models is feasible with high accuracy.
  • The developed system utilizing Random Forest classification shows promise for forensic applications.
  • Automated analysis of dental imagery provides a valuable tool for identification in forensic investigations.