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Proceedings Paper

Image segmentation for automated dental identification
Author(s): Eyad Haj Said; Diaa Eldin M. Nassar; Hany H. Ammar
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Paper Abstract

Dental features are one of few biometric identifiers that qualify for postmortem identification; therefore, creation of an Automated Dental Identification System (ADIS) with goals and objectives similar to the Automated Fingerprint Identification System (AFIS) has received increased attention. As a part of ADIS, teeth segmentation from dental radiographs films is an essential step in the identification process. In this paper, we introduce a fully automated approach for teeth segmentation with goal to extract at least one tooth from the dental radiograph film. We evaluate our approach based on theoretical and empirical basis, and we compare its performance with the performance of other approaches introduced in the literature. The results show that our approach exhibits the lowest failure rate and the highest optimality among all full automated approaches introduced in the literature.

Paper Details

Date Published: 17 February 2006
PDF: 10 pages
Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640X (17 February 2006); doi: 10.1117/12.650757
Show Author Affiliations
Eyad Haj Said, West Virgina Univ. (United States)
Diaa Eldin M. Nassar, West Virgina Univ. (United States)
Hany H. Ammar, West Virgina Univ. (United States)

Published in SPIE Proceedings Vol. 6064:
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Nasser M. Nasrabadi; Edward R. Dougherty; Jaakko T. Astola; Syed A. Rizvi; Karen O. Egiazarian, Editor(s)

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