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

Unsupervised caries detection in non-standardized bitewing dental X-Rays.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2017.In recent years dental image processing has become a useful tool in aiding healthcare professionals diagnose patients by reducing some of the problems inherent with dental radiographs. Despite advances in the eld, accurate diagnoses of dental caries using Comptuer-aided Diagnosis (CAD) tools are still problematic due to the non-uniform nature of dental X-rays. The reason as to why accurate diagnoses are problematic is in part due to exisiting systems utilizing a supervised learning model for their diagnostic algorithms. Using this approach results in a detection system which is trained to identify caries under speci c conditions. When the input images vary greatly from the training set, these systems have a tendency to misdiagnose patients or miss possible caries altogether. A method for the segmentation of teeth in periapical X-Rays is presented in this dissertation as well as a method for the detection of caries across a variety of non-uniform X-ray images using an unsupervised learning model. The diagnostic method proposed in this dissertation uses an assessment protocol similar to how dentists evaluate the presence of caries. Using this assessment protocol results in caries being evaluated relative to the image itself and not evaluated relative to a set of identi ers obtained from a learning model. The viability of an unsupervised learning model, and its relative e ectiveness of accurately diagnosing dental caries when compared to current systems, is indicated by the results detailed in this dissertation. The proposed model achieved a 96% correct diagnostic which proved competitive with existing models

Segmentação de imagens dentárias por binarização, agrupamento e contornos ativos

A imagiologia odontológica é cada vez mais utilizada como meio complementar de diag-nóstico médico, de acompanhamento e de avaliação de planos de tratamento. Para além destas aplicações, a imagem resultante de radiografia dentária é também utilizada na odon-tologia forense para identificação de cadáveres humanos. A segmentação de imagens dentárias visa a obtenção do contorno bidimensional do dente ou de estruturas internas a esse dente. A execução desta tarefa implica a escolha do método de acordo com o tipo de imagem. Neste trabalho procura-se saber que o método deve ser escolhido em função do tipo de imagem. Para tal faz-se uma recolha das principais técnicas de processamento de imagem utilizadas na segmentação e selecionam-se três métodos representativos das diferentes abordagens possíveis: binarização, agrupamento e contornos ativos. A binarização é completada com técnicas de pré-processamento como a filtragem e as ope-rações morfológicas. O agrupamento utiliza o método do k-means para definir a constitui-ção das várias classes em que a imagem é dividida. Os contornos ativos resultam da im-plementação do método iterativo de Chan-Vese que procura os pontos de maior variação da tonalidade. Os testes efetuados mostram que todos os métodos permitem obter uma segmentação apro-ximada do dente e que todos têm dificuldades em separar a área correspondente ao dente da área correspondente ao tecido ósseo. No entanto o método dos contornos ativos parece mais eficiente nesta região. Os métodos da binarização e do agrupamento permitem identi-ficar estruturas internas ao dente, mas apensas o agrupamento permite identificar inserções de amálgama.The medical imaging is increasingly relevant as a supplementary mean of medical diagno-sis. An area where it is essentially used is dentistry, where the use of dental radiographs allows the development of more effective diagnostic, monitoring and evaluation of appro-priate treatment plans. The dental radiography is also a valuable tool in the imaging area of forensic dentistry by allowing the identification of human beings. Dental image segmentation aims to achieve the two-dimensional contour of the tooth or the contour of its internal structures. The execution of these tasks involves the choice of the method according to the characteristics of the image. This research work seeks to determine which method should be chosen depending on the type of image. Three segmentation methods representative of different possible approach-es, namely thresholding, clustering and active contours are selected and tested on a set of four images. The thresholding method is completed with preprocessing techniques such as filtering and morphological operations. The clustering method uses the k-means algorithm to define the composition of the various clusters in which the image is divided. The active contours method results on the implementation of the Chan-Vese iterative algorithm that looks for the points of greatest variation of the intensity. The experiments show that all methods can achieve an approximated segmentation of the tooth. All of them have difficulties in separating the area corresponding to the tooth from the area corresponding to the bone. However, the active contour method seems more effi-cient in this area. The thresholding and clustering methods allow identifying internal struc-tures of the tooth, but only clustering allows identifying inserted amalgam