Panoramic Dental X-ray Images Research Articles

Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge.

Panoramic dental X-ray imaging is an established method for the diagnosis of dental problems. However, the resolution of panoramic dental X-ray images is relatively low. Thus, early lesions are often overlooked. As the first step in the development of a computer-aided diagnosis scheme for panoramic dental X-ray images, we propose a computerized method for the segmentation of teeth using U-Net with a loss function weighted on the tooth edge. Our database consisted of 162 panoramic dental X-ray images. The training dataset consisted of 102 images, while the remaining 60 images were used as the test dataset. The loss function obtained by the cross entropy (CE) in the entire image is usually used in training U-Net. To improve the segmentation accuracy of the tooth edge, a loss function weighted on the tooth edge is proposed by adding the CE in the tooth edge region to the CE for the entire image. The mean Jaccard index and Dice index for U-Net with the loss function combining the CEs for the entire image and tooth edge were 0.864 and 0.927, respectively, which were significantly larger than those for U-Net with the CE for the entire image (0.802 and 0.890, p < 0.001) and U-Net with the CE for the tooth edge (0.826 and 0.905, p < 0.001). U-Net with the new loss function exhibited a higher segmentation accuracy of the tooth in panoramic dental X-ray images than that obtained by U-Net with the conventional loss function.

Individual tooth detection and identification from dental panoramic X-ray images via point-wise localization and distance regularization

Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization loss. The proposed network initially performs center point regression for all the anatomical teeth (i.e., 32 points), which automatically identifies each tooth. A novel distance regularization penalty is employed on the 32 points by considering L2 regularization loss of Laplacian on spatial distances. Subsequently, teeth boxes are individually localized using a multitask neural network on a patch basis. A multitask offset training is employed on the final output to improve the localization accuracy. Our method successfully localizes not only the existing teeth but also missing teeth; consequently, highly accurate detection and identification are achieved. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of teeth detection by 15.71 % compared to the best performing method. The accuracy of identification achieved a precision of 0.997 and recall value of 0.972. Moreover, the proposed network does not require any additional identification algorithm owing to the preceding regression of the fixed 32 points regardless of the existence of the teeth.

Biological Gender Estimation from Panoramic Dental X-ray Images Based on Multiple Feature Fusion Model

In a catastrophe, the conventional biological characteristics of the victims will be destroyed. Forensic odontology is the main method to identify the victims. Estimating the gender of the victims has a significant meaning and can greatly help identify the victims. In this paper, we propose a new automatic method to the gender estimation from panoramic dental X-ray images based on improved convolutional neural network with multiple feature fusion module. Our dataset includes 19,976 panoramic dental X-ray images from Chinese patients. The method we propose can estimate 142 images per second on the conventional computing equipment and it achieves state-of-the-art performance, accuracy of 94.6% ± 0.58%, in our dataset. Our model is interpreted by perturbation-based forward propagation approaches, and the results show that focus of our method on the area of mandible and teeth is reliable which is in accordance with forensic practice.

Jaw and Teeth Segmentation on the Panoramic X-Ray Images for Dental Human Identification.

Due to the damage to biometric properties in the event of natural disasters, like fire or earthquakes, it is very difficult to identify human remains. As teeth are more durable than other biometric properties, identifying information obtained from them is much more reliable. Therefore, in cases where alternative biometric properties cannot be obtained or used, information taken from teeth may be used to identify a person's remains. In recent years, many studies have shown how the identification process, previously performed manually by a forensic dental specialist, can be made faster and more reliable with the assistance of computers and technology. In these studies, the x-ray image is subdivided into meaningful parts, including jaws and teeth, and dental properties are extracted and matched. In order to extract the features accurately and ensure better matching, it is important to segment images properly. In this study, (i) lower and upper jaw and (ii) tooth separation was performed to segment panoramic dental x-ray images to assist in identifying human remains. To separate the jaws, a novel meta-heuristic optimization-based model is proposed. To separate teeth, a user-assisted, semi-automatic approach is presented. The proposed methods have been performed with a computer program. The results of the implementation of these methods of jaw and tooth separation in panoramic tooth images are encouraging.

TSASNet: Tooth segmentation on dental panoramic X-ray images by Two-Stage Attention Segmentation Network

Tooth segmentation acts as a crucial and fundamental role in dentistry for doctors to make diagnosis and treatment plans. In this paper, we propose a Two-Stage Attention Segmentation Network (TSASNet) on dental panoramic X-ray images to address the issues suffered in the tooth boundary and tooth root segmentation task which are caused by the low contrast and uneven intensity distribution. We firstly adopt an attention model which is embedded with global and local attention modules to roughly localize the tooth region in the first stage. Without any interactive operator, the attention model so constructed can automatically aggregate pixel-wise contextual information and identify coarse tooth boundaries. To better obtain final boundary information, we use a fully convolutional network as the second stage to further segment the real tooth area from the attention maps obtained from the first stage. The effectiveness of TSASNet is substantiated on the benchmark dataset containing 1,500 dental panoramic X-ray images, our proposed method achieves 96.94% of accuracy, 92.72% of dice and 93.77% of recall, significantly superior to the current state-of-the-art methods.

Dental Age Estimation Based on X-ray images

Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences. Various statistical approaches have proposed by considering the teeth and mandible. However, building automated dental age estimation based on machine learning techniques needs more research efforts. In this paper, an automated dental age estimation is proposed using transfer learning. In the proposed approach, features are extracted using two deep neural networks namely, AlexNet and ResNet. Several classifiers are proposed to perform the classification task including decision tree, k-nearest neighbor, linear discriminant, and support vector machine. The proposed approach is evaluated using a number of suitable performance metrics using a dataset that contains 1429 dental X-ray images. The obtained results show that the proposed approach has a promising performance.

Automated Maxillofacial Segmentation in Panoramic Dental X-Ray Images Using an Efficient Encoder-Decoder Network

The panoramic dental X-ray images are an essential diagnostic tool used by dentists to detect the symptoms in an early stage and develop appropriate treatment plans. In recent years, deep learning methods have been applied to achieve tooth segmentation of dental X-rays, which aims to assist dentists in making clinical decisions. Because the original images contain plenty of useless information, it is necessary to extract the region-of-interest (ROI) to obtain more accurate results by focusing on the maxillofacial region. However, a fast and accurate maxillofacial segmentation without hand-crafted features is challenging due to the poor image quality. In this study, we create a large maxillofacial dataset and propose an efficient encoder-decoder network model named EED-Net to solve this problem. This dataset consists of 2602 panoramic dental X-ray images and corresponding segmentation masks annotated by the trained experts. Based on the original structure of U-Net, our model structure contains three major modules: a feature encoder, a corresponding decoder, and a multipath feature extractor that connects the encoding path and the decoding path. In order to obtain more semantic features from the depth and breadth, we replace the convolution layer with the residual block in the encoder and adopt Inception-ResNet block in the multipath feature extractor. Inspired by the skip connection in FCN-8s, the lightweight decoder has the same channel dimension as the number of segmented objects. Besides, a weighted loss function is used to enhance segmentation accuracy. The comprehensive experimental results on the new dataset demonstrate that our model achieves better accuracy and speed trade-offs for maxillofacial segmentation than the latest methods.

Caries Detection in Panoramic Dental X-ray Images Using Support Vector Machine

Caries Detection in Panoramic Dental X-ray Images Using Support Vector Machine

The determination of age and gender by implementing new image processing methods and measurements to dental X-ray images

All of the features used to identify and distinguish people from others constitute that person's identity. For any reason, a person's identity may need to be identified and distinguished from other people. Authorities provided the credentials of a living or dead person in such cases from the forensic institutions. The identification process must be done correctly. In this study, specific measurement calculations were made on dental x-ray images to determine age and gender. Age and gender information of the persons were systematically determined by working with panoramic dental x-ray images. Panoramic dental x-ray images were taken out of bounds, and a total of 1315 tooth images and 162 different tooth groups were used. These images have been subjected to 3 different preprocess operations. Each preprocessed image is recorded in different (M1, M2, M3) folders. Then, image processing techniques applied for the first time to the tooth images (Area, Perimeter, Center of gravity, Similarity ratio, Radius calculation) were applied. This information of the teeth is also kept in separate XML (XMLlist-1, 2, 3) files. The application was developed in C # programming language. The user loads the tooth image into the application. This image can be predicted by comparing it with the comparison group (area, etc.) after the desired preprocessing. The highest estimated age and gender estimates are 100% and 95%, respectively.

Human identification with dental panoramic radiographic images

Dental panoramic radiographic images are commonly used as biometrics for human identification. In this study, a novel method is presented for identifying humans by matching 2D panoramic dental X-ray images. Each tooth is first identified and labeled with support vector machines and probabilistic graphical models. Missing teeth and dental restorations are also detected. Then, matching scores between images are calculated according to tooth-wise appearance and geometric similarities by taking dental restorations into account. The method is tested on a dataset including 206 dental panoramic X-ray images of 170 different subjects. The proposed method has 81% rank-1 accuracy and 89% rank-2 accuracy.

The Algorithm of Image Edge Detection on Panoramic Dental X-Ray using Multiple Morphological Gradient (mMG) Method

Dental caries are tooth decay caused by bacterial infections . It is commonly known as cavities. This infection causes demineralization and hence destruction of the hard tissues of the teeth. Diagnosis of dental caries is conventionally carried out with the help of radiographic films. This research aims to develop some algorithm of the mMG method in identifying dental caries based using digital panoramic dental x-ray images. This paper presents an algorithm of using digital panoramic dental x-ray images to detect dental caries. Type of algorithm used in this study is normal mMG, Enhancement mMG, and Smooth mMG. This study makes use of MATLAB and it performs dental caries detection in three algorithms. A dataset of 225 digital panoramic dental x-ray images in .png format is used to edge detection of the object in dental. The results are helpful to identify such caries from the tooth.

A novel method for landmark-based personal identification on panoramic dental radiographic and computed tomographic images

Abstract Personal identification based on dental work is a well-known and useful method for post-mortem identification. Recently, several odontologists have matched reconstructed post-mortem computed tomographic (CT) panoramic images of unidentified bodies with ante-mortem dental panoramic X-ray images for identification purposes. However, it is not always possible to obtain useful information from these panoramic X-ray and CT images. To obtain more accurate information for personal identification, we focused on the anatomical structures of the maxilla and mandible rather than dental work on the images. The aim of the present research is to develop a novel method for the personal identification of unidentified bodies, based on landmarks on the tooth sockets, which are not notably subject to post-mortem changes. We calculated the Procrustes distance and Pearson's correlation coefficients of the landmarks of two sample images, and used the results to correctly identify the subjects from among a pool of 100 candidates, without having to rely on information on dental work. We conclude that this method is a reliable tool and can be easily and rapidly applied for victim and missing person identification.

Removal of the Shadow of Cervical Vertebrae from Panoramic X-Ray Images with a Tomosynthesis Method

The purpose of this study is to remove the shadow of cervical vertebrae from dental panoramic x-ray images with a tomosynthesis method and improve the contrast of details in both the teeth and jaw bones. To measure the shift-amount at each angular position that was required for reconstruction of panoramic x-ray images of the dental arch, strip images of a calibration phantom were acquired. Then, a shift-amount table was prepared from these images, and the other shift-amount table, which was used to reconstruct a panoramic image of the cervical vertebrae, was prepared by inverting the curve of the shift-amount table upside down. Using these two tables, images focused on the dental arch and cervical vertebrae of a patient were made with the original strip data of the patient. The shadow of the cervical vertebrae appearing on the image focused on the dental arch was removed using the two above-mentioned images and blurring functions defined at two focusing geometries. The validity of the proposed method was evaluated with clinically acquired data of two patients. The shadow of the cervical vertebrae was successfully eliminated, and the contrast of the front teeth and detailed structures of the jaw bones was improved. The results of the experiments showed that our proposed method was significantly effective in removing the shadow of the cervical vertebrae from conventional panoramic x-ray images.

Segmentation and Feature Extraction of Panoramic Dental X-Ray Images

Automating the process of analysis of Panoramic X-Ray images is important to help dentist procedures and diagnosis. Tooth segmentation from the radiographic images and feature extraction are essential steps. The authors propose a segmentation approach based on mathematical morphology, quadtree decomposition for mask generation, thresholding, and snake models. The feature extraction stage is steered by a shape model based on Principal Component Analysis (PCA). First, the authors take the quadtree decomposition of a low-pass version of the original image and select the smallest blocks to generate a mask. Then, the original image is processed by Otsu’s thresholding. The result is improved by morphological operators and the quadtree mask is applied to address overlapping, a common problem in X-ray images. The obtained regions are searched and the larger ones are selected to find tooth candidates. The boundary of the obtained regions are extracted and aligned with the shape model in order to recognize the target tooth (molar). The selected curve is used in a search method to initialize a snake technique. Finally, morphometric data extraction is performed to obtain tooth measurements for dentist diagnosis. Experiments show the advantages of the proposed method to extract teeth from X-Ray images and discuss its drawbacks.