NDLI: Combining Matching Algorithms for Human Identification using Dental X-Ray Radiographs

Content Provider	IEEE Xplore Digital Library
Author	Nomir, O. Abdel-Mottaleb, M.
Copyright Year	2007
Description	Author affiliation: Mansoura Univ., Mansoura (Nomir, O.; Abdel-Mottaleb, M.)
Abstract	The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper we present a system for identifying individuals from their dental X-ray records. Given a dental record, usually a postmortem (PM) radiograph, the system searches a database of ante mortem (AM) radiographs and retrieves the best matches from the database. The system automatically segments dental X-ray images into individual teeth and extracts representative feature vectors for each tooth, which are later used for retrieval. The system integrates one method for teeth segmentation, and two different methods for representing and matching teeth. The first matching method represents each tooth contour by signature vectors obtained at salient points on the contour of the tooth. The second method uses hierarchical Chamfer distance for matching AM and PM teeth to reduce the search space and accordingly reduce the retrieval time. Given a query PM image, and according to a matching distance, AM radiographs that are most similar to the PM image, are found and presented to the user using the two matching methods. The experimental results show that the system is robust. We studied the performance of the different modules of the system as well as the results effusing the matching techniques.
File Size	362110
File Format	PDF
ISBN	9781424414369
ISSN	15224880
DOI	10.1109/ICIP.2007.4379179
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Dentistry Radiography Teeth X-ray imaging Image databases Spatial databases Image segmentation Forensics Information retrieval fusion Forensic dentistry biometrics
Content Type	Text
Resource Type	Article

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