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Jaw and Teeth Segmentation on the Panoramic X-Ray Images for Dental Human Identification

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Abstract

Due to the damage to biometric properties in the event of natural disasters, like fire or earthquakes, it is very difficult to identify human remains. As teeth are more durable than other biometric properties, identifying information obtained from them is much more reliable. Therefore, in cases where alternative biometric properties cannot be obtained or used, information taken from teeth may be used to identify a person’s remains. In recent years, many studies have shown how the identification process, previously performed manually by a forensic dental specialist, can be made faster and more reliable with the assistance of computers and technology. In these studies, the x-ray image is subdivided into meaningful parts, including jaws and teeth, and dental properties are extracted and matched. In order to extract the features accurately and ensure better matching, it is important to segment images properly. In this study, (i) lower and upper jaw and (ii) tooth separation was performed to segment panoramic dental x-ray images to assist in identifying human remains. To separate the jaws, a novel meta-heuristic optimization-based model is proposed. To separate teeth, a user-assisted, semi-automatic approach is presented. The proposed methods have been performed with a computer program. The results of the implementation of these methods of jaw and tooth separation in panoramic tooth images are encouraging.

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Authors and Affiliations

  1. Software Engineering Department, Of Technology Faculty, Karadeniz Technical University, Trabzon, Turkey

    Mustafa Hakan Bozkurt

  2. Electrical and Electronics Eng. Department, Engineering Faculty, Ondokuz Mayis University, Samsun, Turkey

    Serap Karagol

Authors
  1. Mustafa Hakan Bozkurt
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  2. Serap Karagol
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Correspondence to Serap Karagol.

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Bozkurt, M.H., Karagol, S. Jaw and Teeth Segmentation on the Panoramic X-Ray Images for Dental Human Identification. J Digit Imaging 33, 1410–1427 (2020). https://doi.org/10.1007/s10278-020-00380-8

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  • DOI: https://doi.org/10.1007/s10278-020-00380-8

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