Image segmentation for automated dental identification

Eyad Haj Said; Diaa Eldin M. Nassar; Hany H. Ammar

doi:10.1117/12.650757

17 February 2006 Image segmentation for automated dental identification

Eyad Haj Said, Diaa Eldin M. Nassar, Hany H. Ammar

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 60640X (2006) https://doi.org/10.1117/12.650757
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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.

Citation Download Citation

Eyad Haj Said, Diaa Eldin M. Nassar, and Hany H. Ammar "Image segmentation for automated dental identification", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640X (17 February 2006); https://doi.org/10.1117/12.650757

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