Hidden Markov random field and FRAME modelling for TCA-image analysis

Tooth Cementum Annulation (TCA) is an age estimation method carried out on thin cross sections of the root of human teeth. Age is computed by adding the tooth eruption age to the count of annual incremental lines that are called tooth rings and appear in the cementum band. Algorithms to denoise and segment the digital image of the tooth section are considered a crucial step towards computer-assisted TCA. The approach pursued in this paper relies on modelling the images as hidden Markov random fields, where gray values are assumed to be pixelwise conditionally independent and normally distributed, given a hidden random field of labels. These unknown labels have to be estimated to segment the image. To account for long-range dependence among the observed values and for periodicity in the placement of tooth rings, the Gibbsian label distribution is specified by a potential function that incorporates macro-features of the TCA-image (a FRAME model). Estimation of the model parameters is carried out by an EM-algorithm that exploits the mean field approximation of the label distribution. Segmentation is based on the predictive distribution of the labels given the observed gray values. KEYWORDS: EM, FRAME, Gibbs distribution, (hidden) Markov random field, mean field approximation, TCA

A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm

Biofilm is a formation of microbial material on tooth substrata. Several methods to quantify dental biofilm coverage have recently been reported in the literature, but at best they provide a semi-automated approach to quantification with significant input from a human grader that comes with the graders bias of what are foreground, background, biofilm, and tooth. Additionally, human assessment indices limit the resolution of the quantification scale; most commercial scales use five levels of quantification for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current state-of-the-art techniques in automatic plaque quantification fail to make their way into practical applications owing to their inability to incorporate human input to handle misclassifications. This paper proposes a new interactive method for biofilm quantification in Quantitative light-induced fluorescence (QLF) images of canine teeth that is independent of the perceptual bias of the grader. The method partitions a QLF image into segments of uniform texture and intensity called superpixels; every superpixel is statistically modeled as a realization of a single 2D Gaussian Markov random field (GMRF) whose parameters are estimated; the superpixel is then assigned to one of three classes (background, biofilm, tooth substratum) based on the training set of data. The quantification results show a high degree of consistency and precision. At the same time, the proposed method gives pathologists full control to post-process the automatic quantification by flipping misclassified superpixels to a different state (background, tooth, biofilm) with a single click, providing greater usability than simply marking the boundaries of biofilm and tooth as done by current state-of-the-art methods.Comment: 10 pages, 7 figures, Journal of Biomedical and Health Informatics 2014. keywords: {Biomedical imaging;Calibration;Dentistry;Estimation;Image segmentation;Manuals;Teeth}, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758338&isnumber=636350

SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%.  Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology

An Adaptive Thresholding Method for Segmenting Dental X-Ray Images

Thresholding is a way of segmenting an image into foreground and background according to a fixed constant value called a threshold. Image segmentation based on a constant threshold leads to unsatisfactory results with dental X-ray images due to the uneven distribution of pixel intensity. In this paper, an adaptive thresholding method is proposed to attain promising segmentation results for dental X-ray images. The Mean, Median, Midgrey, Niblack, and OTSU thresholding methods are utilized to delineate the acceptable range of threshold values to be applied for segmenting X-ray images. Experimental results showed that the Median method provides consistency in achieving the best range of threshold values which is between 57 and 86 in greyscale

Nonparametric joint shape learning for customized shape modeling

We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation

Exploring the use of AI in odontology for paediatric patients : a systematic integrative review

Introdução: A inteligência artificial (IA) é a capacidade que um computador tem de reproduzir um determinado raciocínio, planeamento e mesmo a criatividade semelhante à do ser humano. A relevância desta revisão reside na oportunidade de explorar a importância da IA na nossa vida moderna, no futuro fluxo de trabalho dos consultórios dentários, sendo a literatura escassa no âmbito da IA em Odontopediatria. Objetivo: Determinar de que forma a IA pode ser aplicada em odontologia pediátrica. Materiais e métodos: Foi realizada uma pesquisa bibliográfica na base de dados PubMed. Os resultados incluem estudos publicados que cumprem os critérios no período de 2013 até 23 de janeiro 2023. Resultados: Várias pesquisas foram realizadas em pacientes pediátricos em relação à estimativa de idade dentária, posicionamento dentário e diagnóstico de cárie. A maioria desses estudos encontrou conclusões positivas relativamente à precisão dos modelos de aprendizagem profunda aplicados à análise de imagens. Discussão: Na literatura enfatiza a importância de investigações adicionais com amostras mais significativas. A aplicação desses modelos no fluxo de trabalho odontológico e as preocupações éticas foram também discutidas. Conclusão: A AI mostra resultados promissores no campo da odontopediatria, mas mais pesquisas são necessárias, a regulamentação ética sobre privacidade de dados precisa ser adotada e aplicada.Introduction: Artificial intelligence (AI) is the ability of a computer to reproduce a certain reasoning, planning and even creativity similar to that of a human being. The relevance of this review lies in the opportunity to explore the importance of AI in our modern life, in the future workflow of dental offices, since literature is scarce in the field of AI in Paediatric Dentistry. Aim: To determine whether AI can be applied in paediatric dentistry. Materials and methods: A literature search was conducted in the PubMed database. The results include published studies meeting the criteria in the period from 2013 to January 23, 2023. Results: Several researches have been conducted in paediatric patients regarding dental age estimation, tooth positioning and caries diagnosis. Most of these studies found positive conclusions regarding the accuracy of deep learning models applied to image analysis. Discussion: In the literature the importance of further investigations with more significant samples is emphasised. The application of these models in the dental workflow and ethical concerns were also discussed. Conclusion: AI shows promising results in the field of paediatric dentistry, but more research is needed, ethical regulations on data privacy need to be adopted and enforced

Tooth segmentation using dynamic programming-gradient inverse coefficient of variation

Teeth provide meaningful clues of an individual. The growth of the teeth is correlated with the individual age. This correlation is widely used to estimate age of an individual in applications like conducting forensic odontology, immigration, and differentiating juveniles and adolescents. Current forensic dentistry largely depends on laborious investigation process that is performed manually and can be influenced by human factors like fatigue and inconsistency. Digital panoramic radiograph dental images allow noninvasive and automatic investigation to be performed. This paper presents analyses on third molar tooth segmentation for the population in Malaysia, ranging from persons age of 5 years old to 23 years old. Two segmentation techniques: gradient inverse coefficient of variation with dynamic programming (DP-GICOV) and Chan-Vese (CV) were employed and compared. Results demonstrated that the accuracy of DP-GICOV and CV were 95.3%, and 81.6%, respectively

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface.Comment: Accepted in Medical Image Analysi