Dental Identification System Research Articles

Dental Identification System in Public Health: Innovations and Ethical Challenges: A Narrative Review.

Dental identification systems (DISs) encompass various techniques used for forensic identification, serving as alternatives or complements to genetic methods. Technologies such as microchip implants, prosthetic inscriptions, microSD cards, and identification plaques have been proposed to address limitations in comparative methods, offering streamlined processes for forensic experts. This study reviews current and potential DIS implementations, emphasizing cost-effectiveness and community benefits. Literature analysis from PubMed (2008-2024) yielded 17 relevant articles on implantable DISs, enabling direct subject identification via teeth or prostheses. The integration of DIS aims to enhance accuracy and speed in personal profiling and legal identification, promoting technology transfer in dentistry. It will be necessary to develop strict privacy regulations to protect patient data and establish ethical guidelines for their use. The study's aim is to highlight that the universal adoption of DISs could mitigate healthcare disputes and facilitate data exchange in clinical settings, which is particularly beneficial for vulnerable populations.

Dental X-ray Identification System Based on Association Rules Extracted by k-Symbol Fractional Haar Functions

Several identification approaches have recently been employed in human identification systems for forensic purposes to decrease human efforts and to boost the accuracy of identification. Dental identification systems provide automated matching by searching photographic dental features to retrieve similar models. In this study, the problem of dental image identification was investigated by developing a novel dental identification scheme (DIS) utilizing a fractional wavelet feature extraction technique and rule mining with an Apriori procedure. The proposed approach extracts the most discriminating image features during the mining process to obtain strong association rules (ARs). The proposed approach is divided into two steps. The first stage is feature extraction using a wavelet transform based on a k-symbol fractional Haar filter (k-symbol FHF), while the second stage is the Apriori algorithm of AR mining, which is applied to find the frequent patterns in dental images. Each dental image’s created ARs are saved alongside the image in the rules database for use in the dental identification system’s recognition. The DIS method suggested in this study primarily enhances the Apriori-based dental identification system, which aims to address the drawbacks of dental rule mining.

Teeth Segmentation of Bitewing X-Ray Images Using Wavelet Transform

Within the recent twenty years, the dental X-ray images have widely been employed in forensic odontology for human identification, particularly where mass disasters happen. In this paper, a novel method is proposed for the process of teeth segmentation and individual teeth isolation of Bitewing X-ray radiographs. The main objective of this study is to develop an automatic teeth segmentation approach that can be used in an Automated Dental Identification System (ADIS). The proposed method is based on separating teeth according to edge lines between crowns of teeth. It comprises four phases as image enhancement, edge detection by using wavelet transform, Region of Interest (ROI) definition, and morphological processing. Image enhancement in our case is done by image sharpening using a Butterworth high pass filter. Directional changes of the image and a blurred version of it are obtained by wavelet transform in the second phase. In ROI definition the upper and lower jaws are first separated using the integral intensity projection and then a region containing the desired edge lines are defined. In the final stage, some morphological operations are applied to isolate the teeth based on separating edge lines. The evaluation of the teeth segmentation is measured by isolating accuracy and visual inspection. Experimental results with 90.6% isolation accuracy of total 681 teeth illustrate that the proposed method is more efficient that the existing algorithms.

Automatic human identification from panoramic dental radiographs using the convolutional neural network

Human identification is an important task in mass disaster and criminal investigations. Although several automatic dental identification systems have been proposed, accurate and fast identification from panoramic dental radiographs (PDRs) remains a challenging issue. In this study, an automatic human identification system (DENT-net) was developed using the customized convolutional neural network (CNN). The DENT-net was trained on 15,369 PDRs from 6300 individuals. The PDRs were preprocessed by affine transformation and histogram equalization. The DENT-net took 128 × 128 × 7 square patches as input, including the whole PDR and six details extracted from the PDR. Using the DENT-net, the feature extraction took around 10 milliseconds per image and the running time for retrieval was 33.03 milliseconds in a 2000-individual database, promising an application on larger databases. The visualization of CNN showed that the teeth, maxilla, and mandible all contributed to human identification. The DENT-net achieved Rank-1 accuracy of 85.16% and Rank-5 accuracy of 97.74% for human identification. The present results demonstrated that human identification can be achieved from PDRs by CNN with high accuracy and speed. The present system can be used without any special equipment or knowledge to generate the candidate images. While the final decision should be made by human specialists in practice. It is expected to aid human identification in mass disaster and criminal investigation.

Automated Identification from Dental Data (AutoIDD): A new development in digital forensics

There has been a significant expansion in the use of 3-dimensional (3D) dental images in recent years. In the field of forensic odontology, an automated 3D dental identification system could enhance the identification process. This study presents a novel method for automated human dental identification using 3D digital dental data by utilising a dental identification scenario. The total study sample was divided into two groups: Group A (120 dental models) and Group B (120 Intra-oral scans-IOS). Group A data was composed of 3D scanned dental models of post-orthodontic treated patients (30 maxillary and 30 mandibular). This data was considered as AM digital data. To generate an identical sample, the dental casts (60) of the same patients were retrieved and laser scanned. These models were considered as PM digital data. Group B data (IOS) was obtained from 30 study participants. To reconstruct a dental identification scenario 30 maxillary and 30 mandibular IOS were obtained from 30 participants and were considered as IOS-AM. After one year, another set of IOS (60) were acquired from the same participants and were considered as IOS-PM. The results showed that the AutoIDD (Automated Identification from Dental Data) software was consistent in accuracy; capable of differentiating “correct matches” (high match percentage) from “non-matches” (very low percentage) by 3D image superimposition. The match percentage of the maxillary and mandibular IOS ranged from 64 to 100% and 81–100 %, with a mean distance (mm) of 0.094 and 0.093 respectively. This study demonstrated the feasibility of using 3D scans through a new automated software – AutoIDD in digital forensics to assist the forensic expert in confirming the identity of a deceased individual from the available AM dental records.

Detection of missing tooth from dental radiographic and photographic images in forensic odontology

Victim identification using dental radiographs is receiving much attention nowadays, since tooth is the more robust key component for forensic odontology. Missing tooth detection is one of the notable issues in dental identification system. Hence, this work proposes a novel method for identifying the persons by considering the missing tooth in addition with the contours of other teeth. Online region-based active contour model (ORACM) is used for shape extraction, and distance-based matching algorithm is used to identify the person by comparing both ante-mortem and post-mortem dental images. It concentrates on radiographs and photographs of dental images since adequate radiograph images may not be available for all circumstances. The proposed method is well suited for person identification, and it could assist forensic odontologists to identify the victims in an accurate manner.

Detection of Missing Tooth from Dental radiographic and photographic images in Forensic Odontology

Victim identification using dental radiographs is receiving much attention nowadays, since tooth is the more robust key component for forensic odontology. Missing tooth detection is one of the notable issues in dental identification system. Hence, this work proposes a novel method for identifying the persons by considering the missing tooth in addition with the contours of other teeth. Online region-based active contour model (ORACM) is used for shape extraction, and distance-based matching algorithm is used to identify the person by comparing both ante-mortem and post-mortem dental images. It concentrates on radiographs and photographs of dental images since adequate radiograph images may not be available for all circumstances. The proposed method is well suited for person identification, and it could assist forensic odontologists to identify the victims in an accurate manner.

A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.

Segmentation and root localization for analysis of dental radiographs

During lifetime, teeth are exposed to many effects like abrasion, loss and dental treatments. These effects along with natural shapes of teeth form a unique dental frame which contains useful attributes to be used for human identification. Today, there exist automated dental identification systems which are used by forensics of law departments. These systems need to extract dental structures like teeth or roots prior to further analysis. So far, in several studies, much effort has been paid for this task. However, there still exist core problems like automated detection of region of interest (ROI) and segmentation in panoramic dental radiographs with missing teeth. This study aims to present a tool that can be employed to overcome these issues. Unlike previous works, the proposed methodology takes advantage of discrete wavelet transform for more accurate localization of ROI and polynomial regression to form a smooth border, separating upper and lower jaws even in case of absent teeth. Results indicate that the proposed approach can be effectively used for teeth segmentation and root apex detection.

Automated Dental Identification based on Orthogonal Locality Preserving Projection

Major disasters have highlighted the significance of automated dental identification systems. For example in the 9/11 attacks, many victims were identifiable only from pieces of their jaw bones. This is the main motivation for building the Automated Dental Identification System (ADIS). The archiving and retrieving processes of dental records from large databases is a challenging task and has received inadequate attention in the literature. This paper presents a new technique for retrieving dental records for ADIS. Our approach consists of two main stages. The first stage is the preprocessing stage of the dental records that includes segmentation and teeth labeling classification in order to obtain reliable appearance-based, low computational-cost features. In the second stage we propose a technique based on using the Orthogonal Locality Preserving projection algorithm to produce a candidate list. The experiment results show that the proposed approach provides fast and effective results to search and retrieve a list of candidates from the database compared to other approaches suggested in the literature.

Missing tooth identification and teeth numbering in dental X-ray and photographic imaging

In major disasters like air-plane crash, bomb-blast, fire accident and flood, the biometric means like finger print, retina, hand print, palm print, face details etc., may not be available. Hence in such a circumstance there is a need for another reliable, unique and useful means like tooth to develop an automated dental identification system. For dental biometric system the antemortem records has to be matched against the post mortem records. In the dental identification system, missing tooth detection and teeth numbering are few of the notable issues. This paper addresses these issues with the help of spline function, bifurcation techniques and template matching approaches. This review focuses these issues both for radiographic and photographic dental images since adequate radiographic records may not be available in almost all the instances. Experimental evaluation shows satisfied results which will abet the dental biometrics.

An automatic and effective tooth isolation method for dental radiographs

AbstractTooth isolation is a very important step for both computer-aided dental diagnosis and automatic dental identification systems, because it will directly affect the accuracy of feature extraction and, thereby, the final results of both types of systems. This paper presents an effective and fully automatic tooth isolation method for dental X-ray images, which contains up-per-lower jaw separation, single tooth isolation, over-segmentation verification, and under-segmentation detection. The upper-lower jaw separation mechanism is based on a gray-scale integral projection to avoid possible information loss and incorporates with the angle adjustment to handle skewed images. In a single tooth isolation, an adaptive windowing scheme for locating gap valleys is proposed to improve the accuracy. In over-segmentation, an isolation-curve verification scheme is proposed to remove excessive curves; and in under-segmentation, a missing-teeth detection scheme is proposed. The experimental results demonstrate that our method achieves the accuracy rates of 95.63% and 98.71% for the upper and lower jaw images, respectively, from the test database of 60 bitewing dental radiographs, and performs better for images with severe teeth occlusion, excessive dental works, and uneven illumination than that of Nomir and Abdel-Mottaleb’s method. The method without upper-lower jaw separation step also works well for panoramic and periapical images.

Human Identification: A new x-Ray Dental Radiographs Segmentation Algorithm using Graphcut

Law enforcement agencies have been utilizing the biometrics features as the best candidates for forensic identification process. For decades, the identification process was accomplished manually by a forensic expert. Nowadays, it is crucial to automate the process of forensic identification because of the large number of victims confronted in mass disasters. In some circumstances, when the identification takes place after the decay of the body’s tissue, or the victim faces a mass disaster, the dental identification is the key solution for postmortem identification, because the dental features resist the early decay that affects body tissues.In our research, we developed an automatic dental biometric identification system. This paper introduces a new algorithm to automate the process of dental X-ray radiographs segmentation. It segments the dental X-ray radiographs and extracts the contour of each tooth. The introduced segmentation technique is based on graph cut algorithm. The experimental results prove that the introduced algorithm is robust.

Automated Dental Identification System: An Aid to Forensic Odontology

Automated dental identification system is computer-aided software for the postmortem identification of deceased individuals based on dental characteristics specifically radiographs. This system is receiving increased attention because of the large number of victims encountered in the mass disasters and it is 90% more time saving and accurate than the conventional radiographic methods. This technique is based on the intensity of the overall region of tooth image and therefore it does not necessitate the presence of sharp boundary between the teeth. It provides automated search and matching capabilities for digitized radiographs and photographic dental images and compares the teeth present in multiple digitized dental records in order to access their similarity. This paper highlights the functionality of its components and techniques used in realizing these components.

Fusion of Matching Algorithms for Human Identification Using Dental X-Ray Radiographs

The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper, we introduce a system that uses some scenarios to fuse three matching techniques for identifying individuals based on their dental X-ray images. The system integrates a method for teeth segmentation, and three different methods for representing and matching teeth. The first method for matching antemortem (AM) and postmortem (PM) images represents each tooth contour by a set of 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. The third matching method represents each tooth by a small set of features extracted using the forcefield energy function and Fourier descriptors. For each matcher, given a query PM image, AM radiographs that are mostly similar to the PM image, are found and presented to the user. To improve the performance of the system, we present different scenarios to fuse the three matchers. We fuse the matchers using three different approaches at the matching level, the decision level, and using the Bayesian framework. Preliminarily results demonstrate that fusing the matching techniques improves the overall performance of the dental identification system.

Image Registration-Based Approach to Ranking Dental X-Ray Images for Human Forensic Identification

ABSTRACTDental features have been widely used for forensic identification purposes. However, the point-by-point comparison performed by a forensic odontologist in mass disaster situations or missing persons cases would be cumbersome and time-consuming due to the large number of records that would need to be compared. Consequently, a move towards computer-aided dental identification systems is necessary. In this work, we propose a computer-aided framework for matching of dental radiographs based on image registration. Given a postmortem (PM) radiograph with a marked region of interest (ROI), we searched the database of antemortem (AM) radiographs to retrieve a closest match. To express the degree of similarity/overlap between two radiographs, we used the weighted sum of squared differences (SSD) cost function. The method was tested on a database of 571 radiographs belonging to 41 distinct individuals. In 90% of the identification trials, our method ranked the correct match in the top 10%. In all trials, the correct match was among the top 22%. These experiments indicated that matching dental records using the SSD cost function is a viable method for human dental identification.

Importance of dental records for victim identification following the Indian Ocean tsunami disaster in Thailand

To determine the usefulness of dental records for victim identification following the Indian Ocean tsunami disaster in Thailand, and to evaluate the dental identification system in Thailand, the homeland of a large number of the victims. A descriptive study conducted at the Thai Tsunami Repatriation Centre in Phangnga Province one year after the tsunami hit Thailand on the 26th December 2004. The dental records of 3750 dead bodies and 3547 missing persons in the Thai Tsunami Victim Identification (TTVI) database, updated on 12th December 2005, were analysed. The identification rate of missing persons with dental records was significantly higher than that of those without (P<0.01). Most victims identified by dental records were returned home within the first four months after the disaster. Dental records were the primary identifier in 46.2% of those identified. However, among the Thai citizens reported missing, only 2.0% used dental identification, 18.1% had dental charts and 0.8% had dental X-rays. In addition, only 7.4% of Thai dental records could be used for dental identification and one-third of Thai victims remained the majority of those unidentified. Based on this study, the usefulness of dental records for victim identification in a disaster was confirmed. The dental identification system for nationals of Thailand could not work efficiently due to lack of dental records and insufficient recorded detail.

A neural network system for matching dental radiographs

This paper addresses the problem of creating a postmortem identification system by matching image features extracted from dental radiographs. We lay the architecture of a prototype automated dental identification system (ADIS), which tackles the dental image matching problem by first extracting high-level features to expedite retrieval of potential matches and then by low-level image comparison using inherent features of dental images. We propose the use of learnable inherent dental image features for tooth-to-tooth image comparisons. We treat the tooth-to-tooth matching problem as a binary classification problem for which we propose probabilistic models of class-conditional densities. We also propose an adaptive strategic searching technique and use it in conjunction with back propagation in order to estimate system parameters. We present promising experimental results that reflect the value of our approach.

Toward an automated dental identification system

Forensic odontology has long been carried out by forensic experts of law enforcement agencies for postmortem identification. We address the problem of developing an automated system for postmortem identification using dental records (dental radiographs). This automated dental identification system (ADIS) can be used by law enforcement agencies as well as military agencies throughout the United States to locate missing persons using databases of dental x rays of human remains and dental scans of missing or wanted persons. Currently, this search and identification process is carried out manually, which makes it very time-consuming in mass disasters. We propose a novel architecture for ADIS, define the functionality of its components, and describe the techniques used in realizing these components. We also present the performance of each of these components using a database of dental images.

Intelligent dental identification system (IDIS) in forensic medicine

This study reports the design and development of the intelligent dental identification system (IDIS), including its efficiency and reliability. Five hundred patients were randomly selected from the Dental Department at Police General Hospital in Thailand to create a population of 3000 known subjects. From the original 500 patients, 100 were randomly selected to create a sample of 1000 unidentifiable subjects (400 subjects with completeness and possible alterations of dental information corresponding to natural occurrences and general dental treatments after the last clinical examination, such as missing teeth, dental caries, dental restorations, and dental prosthetics, 100 subjects with completeness and no alteration of dental information, 500 subjects with incompleteness and no alteration of dental information). Attempts were made to identify the unknown subjects utilizing IDIS. The use of IDIS advanced method resulted in consistent outstanding identification in the range of 82.61–100% with minimal error 0–1.19%. The results of this study indicate that IDIS can be used to support dental identification. It supports not only all types of dentitions: primary, mixed, and permanent but also for incomplete and altered dental information. IDIS is particularly useful in providing the huge quantity and redundancy of related documentation associated with forensic odontology. As a computerized system, IDIS can reduce the time required for identification and store dental digital images with many processing features. Furthermore, IDIS establishes enhancements of documental dental record with odontogram and identification codes, electrical dental record with dental database system, and identification methods and algorithms. IDIS was conceptualized based on the guidelines and standards of the American Board of Forensic Odontology (ABFO) and International Criminal Police Organization (INTERPOL).