A system for human identification from X-ray dental radiographs

Forensic odontology is the branch of forensics that deals with human identification based on dental features. In this paper, we present a system for automating that process by identifying people from dental X-ray images. Given a dental image of a postmortem (PM), the proposed system retrieves the best matches from an antemortem (AM) database. The system automatically segments dental X-ray images into individual teeth and extracts the contour of each tooth. Features are extracted from each tooth and are used for retrieval. We developed a new method for teeth separation based on integral projection. We also developed a new method for representing and matching teeth contours using signature vectors obtained at salient points on the contours of the teeth. During retrieval, the AM radiographs that have signatures closer to the PM are found and presented to the user. Matching scores are generated based on the distance between the signature vectors of AM and PM teeth. Experimental results on a small database of dental radiographs are encouraging.     

Hierarchical contour matching for dental X-ray radiographs

The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper we present a new algorithm for human identification from dental X-ray images. The algorithm is based on matching teeth contours using hierarchical chamfer distance. The algorithm applies a hierarchical contour matching algorithm using multi-resolution representation of the teeth. Given a dental record, usually a postmortem (PM) radiograph, first, the radiograph is segmented and a multi-resolution representation is created for each PM tooth. Each tooth is matched with the archived antemortem (AM) teeth, which have the same tooth number, in the database using the hierarchical algorithm starting from the lowest resolution level. At each resolution level, the AM teeth are arranged in an ascending order according to a matching distance and 50% of the AM teeth with the largest distances are discarded and the remaining AM teeth are marked as possible candidates and the matching process proceeds to the following (higher) resolution level. After matching all the teeth in the PM image, voting is used to obtain a list of best matches for the PM query image based upon the matching results of the individual teeth. Analysis of the time complexity of the proposed algorithm prove that the hierarchical matching significantly reduces the search space and consequently the retrieval time is reduced. The experimental results on a database of 187 AM images show that the algorithm is robust for identifying individuals based on their dental radiographs.