Dental X-Ray Based Human Identification System for Forensic

Forensic dentistry is an important branch of the forensic science. It is based on the dental characteristic. This method uses the dental features as a biometric tool to identify persons, who their bodies have been affected badly. In the other meaning, the dental biometrics are considered at the absence of tools, such as, DNA, fingerprint, iris etc. for different reasons. This paper presented a biometric system for forensic human identification based on dental X-ray. The aim of this system is to build a database, which contain ante-mortem dental radiograph features (AM), used later for matching with the post-mortem dental radiograph features (PM). These features are Standard Deviation (STD), Euler number and Area extracted from X-ray image of type bite-wing. The investigated X-Ray image goes through three stages algorithm which are: image segmentation, classification and features extraction. The obtained features represents the records of the system database for each tooth individually in distinct person. The proposed system utilizes the Graphical User Interface (GUI) provided from the Visual Studio with the usage of MATLAB software for feature extraction and the SQL Server 2012 environment for database building. The Achieved results show the outperformance of the proposed system in terms of matching and searching accuracy as well as the finding time. In addition the editing and insertion processes are performed in high accuracy and efficiency

Towards Automated Human Identification Using Dental X-ray Images

Masteroppgave informasjons- og kommunikasjonsteknologi - Universitetet i Agder, 2015Systems for automated human identification from dental X-ray images can be used to greatly reduce the necessary effort spent today by dental forensics experts. In this work a new methodology is proposed to create a system for automated dental X-ray identification. The methodology includes both state-of-the-art methods and a novel method for separating a dental X-ray image into individual teeth. The novel method is based on lowest cost pathfinding and is shown to achieve comparable results to the state-of-the-art. In experiments it is able to separate 88.7% of the teeth in the test images correctly. The identification system extracts tooth and dental work contours from the dental X-ray images and uses the Hausdorff-distance measure for ranking persons. The results of testing the system on a new data set show that the new method for dental X-ray separation functions well as a component in a functional identification system and that the methodology on the whole can be used to identify persons with comparable accuracy to related work. In 86% of cases, the correct person is ranked highest. This accuracy increases to 94% when the five highest ranked images are considered. Due to small distances in similarity between highest ranked individuals, doubts are raised concerning the scalability of the method. This is seen as a matter of expansion, such as refining features, rather than redesign. The conclusion is that the proposed methodology, including the path-based method of separation, performs well enough to be worth consideration when designing an automated dental identification system

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

Study and Development of Techniques for 3D Dental Identification

Ph.DDOCTOR OF PHILOSOPH