Thresholding Techniques for Dental Radiographic Images

Imaging techniques play a major role in improving the early detection and diagnostic process that helps dentists to make accurate diagnosis. One of the most useful medical images used by dentists is radiographic image, which is used for the treatment of various dental disorders. Segmentation is a fundamental step as it involves separation of an image into regions corresponding to the objects. A simple and natural way to segment such regions is through thresholding. In this chapter, various thresholding techniques such as Otsu's method for global thresholding and Niblack's, Bersen's, and Sauvola's techniques for local thresholding are extensively explained with the help of dental radiographic images.

Digital Image Enhancement Techniques for Dental Radiographs

Dental radiographs suffer frequently from issues such as low contrast and non-uniform illumination. The first step, indeed a significant step, is to enhance these digital images to prepare them for successful post-processing. This pre-processing stage assists to increase the contrast between the foreground that is the teeth and bone from the background regions. In this chapter, image enhancement methods based on spatial, frequency, and spatial-frequency are implemented. The dental radiographs used are available in the public database. The performance of the enhancement methods is validated using qualitative and quantitative measures. It is observed from the results that the enhancement method aids in improving dental features such as crowns, fillings, and bridges. This enables human identification and diagnostic purpose in the way it is possible to identify a variety of diseases. It also prevents the need for a remake, saving the patient from an additional treatment.

Dental Image Segmentation Using Clustering Techniques and Level Set Methods

Segmentation is necessary for dental images for finding the parts of the teeth, surrounding tissues, and bones. The human identification system in dental methodology is a tedious and time-consuming process. The automatic identification system is the best solution for dental diagnosis and dental treatment systems. Choosing an appropriate region of interest with high accuracy and success rate is a challenging one. This can be attained with the help of proper segmentation methodologies. The segmentation techniques proposed for the root canal treatment are analyzed and compared. Clustering techniques and level set methods with different edge maps are implemented for the proper analysis of segmentation in dental images. Finally, the integration of coherence-enhanced diffusion filtering in basic level set segmentation methodology seems to be effective in improving the segmentation performance of dental images.

Phytochemicals

The chapter gives a picture of the current data on the available anticariogenic natural products and their mechanism of action. Different phytochemicals such as phenols, flavanoids, alkaloids, terpenoids, tannins, lectins, etc. and their anticariogenic efficacy have been discussed in detail. All the data emphasise the fact that the use of natural products is emerging as an effective strategy in the prevention and treatment of dental caries. Consequently, these natural products could be incorporated in toothpastes and other oral hygiene products to promote oral health.

Comparison and Analysis of Dental Imaging Techniques

Fluoride dental decay is the second most common disease around the world. Detection methods for early disease are very crude. Precise oral diagnosis and treatment are very strongly connected to the quality of dental imaging techniques which advances the diagnostic procedure. To study the external appearance of the teeth arches, 2D images are used. CBCT images were used to locate the bone at dental implant sites. Fiberoptic transillumination, fluorescence imaging detects caries. For qualitative and quantitative analysis of dental applications, laser-induced breakdown spectroscopy (LIBS) is used. Electron caries monitor (ECM), fiberoptic transillumination (FOTI), digital fiberoptic transillumination (DIFOTI), quantitative light-induced fluorescence (QLF) are also some of the detection methods used. Hence, in this chapter, the methodologies are analyzed and compared for easy use of the dentist.

Dental Cone Beam Computed Tomography for Trabecular Bone Quality Analysis in Maxilla and Mandible

The stability of a dental implant is one of the most important aspects that decide the success rate of implant treatment. The stability is considerably affected by the strength of trabecular bone present in maxilla and mandible. Thus, finding of trabecular bone strength is a key component for the success of dental implants. The trabecular bone strength is usually assessed by quantity of bone in terms of bone mineral density (BMD). Recently, it has been revealed that along with quantity of bone, strength of the bone also depends on quality features commonly referred as trabecular bone microarchitecture. Since the quality of the trabecular bone is varying across the maxilla and mandible, preoperative assessment of trabecular bone microarchitecture at sub-region of maxilla and mandible are essential for stable implant treatment. Thus, in this chapter, the authors inscribe the quantitative analysis of trabecular bone quality in maxilla and mandible using CBCT images by employing contourlet transform.

Jaya Algorithm-Assisted Evaluation of Tooth Elements Using Digital Bitewing Radiography Images

A considerable number of heuristic procedures are widely implemented to evaluate biomedical images. This chapter proposes an evaluation procedure for digital bitewing radiography (DBR) images using the Jaya algorithm. The proposed procedure implements an image processing technique by integrating of the multi-thresholding and segmentation procedure to extract the essential tooth elements recorded with DBR. In this paper, 80 dental x-ray images are considered for the evaluation. The performance of the proposed procedure is confirmed using a relative assessment between the extracted section and its corresponding ground-truth. The results of this study confirm that, for most of the DBR cases, the proposed approach offers better values of picture likeliness measures. Hence, this technique can be considered for the automated detection of tooth elements from the DBR obtained from clinics.

Recent Trends in 3D Printing of Dental Models

This chapter introduces the recent trend in 3D printing (3DP) in dentistry. The advantage and disadvantages of 3DP are discussed. It elaborates on different types of 3DP techniques involved and their significance. The chapter further discuss about the biomaterial used. It also describes the complete steps involved in 3DP such as image acquisition, modeling, segmentation, and printing techniques. The merits and demerits of the different methodologies pertaining to steps involved in 3DP are illustrated. Rapid prototyping in dental implants is discussed in detail. It ends with review of a case study in implementing the technique.

Thermal Analysis of Artificial Dental Materials Using Numerical Simulation

A numerical investigation of steady state heat transfer phenomenon in human tooth is carried out in this present study. The materials generally used for repairing human tooth are ceramics, gold, structural steel, and copper. These materials are considered for the three-dimensional heat transfer analysis. The shape of the teeth is considered as perfect cubic of dimensions 5mm X 5mm X 1.5mm. The simulation results show that the teeth are subjected to temperatures of varying nature in both X and Y directions whereas Z direction is same. It is found that the temperature is maximum at the top of the teeth and minimum at bottom of the teeth (i.e., root). The contours of the heat transfer analysis for various teeth materials shows that the layers of the teeth are subjected to varying temperature. It is also found that the heat transfer characteristics depend not only on shape and boundary conditions but also on the materials used for the teeth selection.

Teeth and Landmarks Detection and Classification Based on Deep Neural Networks

In the recent decade, deep neural networks have enjoyed rapid development in various domains, including medicine. Convolutional neural networks (CNNs), deep neural network structures commonly used for image interpretation, brought the breakthrough in computer vision and became state-of-the-art techniques for various image recognition tasks, such as image classification, object detection, and semantic segmentation. In this chapter, the authors provide an overview of deep learning algorithms and review available literature for dental image analysis with methods based on CNNs. The present study is focused on the problems of landmarks and teeth detection and classification, as these tasks comprise an essential part of dental image interpretation both in clinical dentistry and in human identification systems based on the dental biometrical information.