A Web-based environment for automated dental identification research

The Criminal Justice Information Services (CJIS), division of the Federal Bureau of Investigations (FBI), include in its strategic plan the creation of an Automated Dental Identification System (ADIS), a Post Mortem Dental Identification System.;This project aims at designing an end-to-end web-interface to meet the requirements of ADIS like Identification, Maintenance and Bridge Modules. In Identification Mode the subject record will be uploaded by the user and the match list is obtained as result. Maintenance Mode enables uploading of reference records and to populate the database with preprocessing data. Bridge Module enables researchers from other universities to use the database designed in WVU. A database is also designed to hold non-dental features like name, age, gender etc and dental features like preprocessing data.;This provides the FBI agents and the Forensic experts the ability to use ADIS from their office desks. This web interface provides an Identification Module, Maintenance Module and Bridge Module. (Abstract shortened by UMI.)

Multi-resolution dental image registration based on genetic algorithm

The Automated Dental Identification System (ADIS) is a Post Mortem Dental Identification System. This thesis presents dental image registration, required for the preprocessing steps of the image comparison component of ADIS. We proposed a multi resolution dental image registration based on genetic algorithms. The main objective of this research is to develop techniques for registration of extracted subject regions of interest with corresponding reference regions of interest.;We investigated and implemented registration using two multi resolution techniques namely image sub sampling and wavelet decomposition. Multi resolution techniques help in the reduction of search data since initial registration is carried at lower levels and results are updated as the levels of resolutions increase. We adopted edges as image features that needed to be aligned. Affine transformations were selected to transform the subject dental region of interest to achieve better alignment with the reference region of interest. These transformations are known to capture complex image distortions. The similarity between subject and reference image has been computed using Oriented Hausdorff Similarity measure that is robust to severe noise and image degradations. A genetic algorithm was adopted to search for the best transformation parameters that give maximum similarity score.;Testing results show that the developed registration algorithm yielded reasonable results in accuracy for dental test cases that contained slight misalignments. The relative percentage errors between the known and estimated transformation parameters were less than 20% with a termination criterion of a ten minute time limit. Further research is needed for dental cases that contain high degree of misalignment, noise and distortions

Novel Techniques for Automated Dental Identification

Automated dental identification is one of the best candidates for postmortem identification. With the large number of victims encountered in mass disasters, automating the process of postmortem identification is receiving an increased attention. This dissertation introduces new approaches for different stages of Automated Dental Identification system: These stages include segmentations, classification, labeling, and matching:;We modified the seam carving technique to adapt the problem of segmenting dental image records into individual teeth. We propose a two-stage teeth segmentation approach for segmenting the dental images. In the first stage, the teeth images are preprocessed by a two-step thresholding technique, which starts with an iterative thresholding followed by an adaptive thresholding to binarize the teeth images. In the second stage, we adapt the seam carving technique on the binary images, using both horizontal and vertical seams, to separate each individual tooth. We have obtained an optimality rate of 54.02% for the bitewing type images, which is superior to all existing fully automated dental segmentation algorithms in the literature, and a failure rate of 1.05%. For the periapical type images, we have obtained a high optimality rate of 58.13% and a low failure rate of 0.74 which also surpasses the performance of existing techniques. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). A dental chart is a key to avoiding illogical comparisons that inefficiently consume the limited computational resources, and may mislead decision-making. We tackle this composite problem using a two-stage approach. The first stage, utilizes low computational-cost, appearance-based features, using Orthogonal Locality Preserving Projections (OLPP) for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and hence to assign each tooth a number corresponding to its location in the dental chart, even in the presence of a missed tooth. The experimental results of teeth classification show that on a large dataset of bitewing and periapical films, the proposed approach achieves overall classification accuracy of 77% and teeth class validation enhances the overall teeth classification accuracy to 87% which is slightly better than the performance obtained from previous methods based on EigenTeeth the performance of which is 75% and 86%, respectively.;We present a new technique that searches the dental database to find a candidate list. We use dental records of the FBI\u27s Criminal Justice Service (CJIC) ADIS database, that contains 104 records (about 500 bitewing and periapical films) involving more than 2000 teeth, 47 Antemortem (AM) records and 57 Postmortem (PM) records with 20 matched records.;The proposed approach consists of two main stages, the first stage is to preprocess the dental records (segmentation and teeth labeling classification) in order to get a reliable, appearance-based, low computational-cost feature. In the second stage, we developed a technique based on LaplacianTeeth using OLPP algorithm to produce a candidate list. The proposed technique can correctly retrieve the dental records 65% in the 5 top ranks while the method based on EigenTeeth remains at 60%. The proposed approach takes about 0.17 seconds to make record to record comparison while the other method based on EigenTeeth takes about 0.09 seconds.;Finally, we address the teeth matching problem by presenting a new technique for dental record retrieval. The technique is based on the matching of the Scale Invariant feature Transform (SIFT) descriptors guided by the teeth contour between the subject and reference dental records. Our fundamental objective is to accomplish a relatively short match list, with a high probability of having the correct match reference. The proposed technique correctly retrieves the dental records with performance rates of 35% and 75% in the 1 and 5 top ranks respectively, and takes only an average time of 4.18 minutes to retrieve a match list. This compares favorably with the existing technique shape-based (edge direction histogram) method which has the performance rates of 29% and 46% in the 1 and 5 top ranks respectively.;In summary, the proposed ADIS system accurately retrieves the dental record with an overall rate of 80% in top 5 ranks when a candidate list of 20 is used (from potential match search) whereas a candidate size of 10 yields an overall rate of 84% in top 5 ranks and takes only a few minutes to search the database, which compares favorably against most of the existing methods in the literature, when both accuracy and computational complexity are considered

Classification of dental x-ray images

Forensic dentistry is concerned with identifying people based on their dental records. Forensic specialists have a large number of cases to investigate and hence, it has become important to automate forensic identification systems. The radiographs acquired after a person is deceased are called the Post-mortem (PM) radiographs, and the radiographs acquired while the person is alive are called the Ante-mortem (AM) radiographs. Dental biometrics automatically analyzes dental radiographs to identify the deceased individuals. While, ante mortem (AM) identification is usually possible through comparison of many biometric identifiers, postmortem (PM) identification is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashes and natural disasters such as Tsunami) most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Under such circumstances, the best candidates for postmortem biometric identification are the dental features because of their survivability and diversity.;In my work, I present two different techniques to classify periapical images as maxilla (upper jaw) or mandible (lower jaw) images and we show a third technique to classify dental bitewing images as horizontally flipped/rotated or horizontally un-flipped/un-rotated. In our first technique I present an algorithm to classify whether a given dental periapical image is of a maxilla (upper jaw) or a mandible (lower jaw) using texture analysis of the jaw bone. While the bone analysis method is manual, in our second technique, I propose an automated approach for the identification of dental periapical images using the crown curve detection Algorithm. The third proposed algorithm works in an automated manner for a large number of database comprised of dental bitewing images. Each dental bitewing image in the data base can be classified as a horizontally flipped or un-flipped image in a time efficient manner

Automated dental identification: A micro-macro decision-making approach

Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. In this work we consider three important problems in automated dental identification beyond the basic approach of tooth-to-tooth matching.;The first problem is on automatic classification of teeth into incisors, canines, premolars and molars as part of creating a data structure that guides tooth-to-tooth matching, thus avoiding illogical comparisons that inefficiently consume the limited computational resources and may also mislead the decision-making. We tackle this problem using principal component analysis and string matching techniques. We reconstruct the segmented teeth using the eigenvectors of the image subspaces of the four teeth classes, and then call the teeth classes that achieve least energy-discrepancy between the novel teeth and their approximations. We exploit teeth neighborhood rules in validating teeth-classes and hence assign each tooth a number corresponding to its location in a dental chart. Our approach achieves 82% teeth labeling accuracy based on a large test dataset of bitewing films.;Because dental radiographic films capture projections of distinct teeth; and often multiple views for each of the distinct teeth, in the second problem we look for a scheme that exploits teeth multiplicity to achieve more reliable match decisions when we compare the dental records of a subject and a candidate match. Hence, we propose a hierarchical fusion scheme that utilizes both aspects of teeth multiplicity for improving teeth-level (micro) and case-level (macro) decision-making. We achieve a genuine accept rate in excess of 85%.;In the third problem we study the performance limits of dental identification due to features capabilities. We consider two types of features used in dental identification, namely teeth contours and appearance features. We propose a methodology for determining the number of degrees of freedom possessed by a feature set, as a figure of merit, based on modeling joint distributions using copulas under less stringent assumptions on the dependence between feature dimensions. We also offer workable approximations of this approach

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

Awareness About Various Newer Technologies Applied in The Field of Forensic Medicine and Forensic Odontology in India Among Health Care Workers and Medical Experts

Background Crime rate has been reported to rise drastically from the year 2017-2019 according to police sources. Forensic odontology is a branch of forensic science which helps in investigating and identification of corpses found in natural disasters and homicide conditions.New developments on technology have made milestone achievements in various fields, likewise even in forensic medicine and forensic odontology the new developments have broken the lab controlled limitation by creating scientific measurements, improving efficacy of the judicial system. Aim: This study aims to determine the awareness about newer technologies over the field of forensic science and odontology among healthcare workers and medical experts. Materials and methods: A set of self evaluable questions were prepared, validated by the principal investigator and guide. The questionnaire was approved by the institutional review board, saveetha dental college, chennai. The questionnaire prepared was then circulated among the healthcare workers and medical experts in the south indian population. The response data was collected, documented and tabulated. The data was analysed using SPSS statistics and results obtained. Results and discussion: The percentage distribution of the selected population based on the awareness questions asked were analysed and tabulated. The correlation between their years of practice and their awareness were studied. The statistics from previous studies were compared with the present study findings to enhance the quality of our findings. Conclusion: There exists a significant level of awareness among the healthcare workers and medical experts. The participants with 0-5 years of practice showed maximum level of awareness and their associations were found to be statistically significant.(p<0.05). Many awareness camps should be held and conducted to increase the knowledge about the recent advances in technologies in the field of forensic sciences and odontology.Saveetha Dental Colleg

Study and Development of Techniques for 3D Dental Identification

Ph.DDOCTOR OF PHILOSOPH