Title: Comparison of missing tooth and dental work detection using dental radiographs in human identification

Authors: G. Jaffino; A. Banumathi; Ulaganathan Gurunathan; J. Prabin Jose

Addresses: Department of ECE, Aditya College of Engineering, Surampalem, Andhra Pradesh, India ' Department of ECE, Thiagarajar College of Engineering, Madurai 15, Tamilnadu, India ' Best Dental Science College, Madurai 15, Tamilnadu, India ' Kamaraj College of Engineering and Technology, Virudhunagar, Tamilnadu, India

Abstract: Victim identification plays a vital role for identifying a person in major disasters at the time of critical situation when all the other biometric information was lost. At that time there is a less chance for identifying a person. The major issues of dental radiographs are dental work and missing or broken tooth was addressed in this paper. This algorithm can be established by comparing both ante mortem (AM) and post-mortem (PM) dental images. This research work is mainly focuses on the detection of dental work and broken tooth or missing tooth, then comparison of active contour model with mathematical model-based shape extraction for dental radiographic images are proposed. In this work, a new mathematical tooth approximation is presented and it is compared with online region-based active contour model (ORACM) is used for shape extraction. Similarity and distance-based technique gives better matching about both the AM and PM dental radiographs. Exact prediction of each method has been calculated and it is validated with suitable performance measures. The accuracy achieved for contour method is 94%, graph partition method is 96% and finally the hit rate of this method is plotted with cumulative matching characteristic (CMC) curve.

Keywords: victim identification; dental work; DW; missing tooth; active contour model; isoperimetric graph partitioning; CMC curve.

DOI: 10.1504/IJBET.2020.106032

International Journal of Biomedical Engineering and Technology, 2020 Vol.32 No.3, pp.217 - 228

Received: 09 Feb 2017
Accepted: 17 May 2017

Published online: 26 Mar 2020 *

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