Dental X-ray Images Research Articles

Dental diagnosis from X-Ray images: An expert system based on fuzzy computing

Abstract Background Computerized medical diagnosis systems from X-Ray images are of great interest to physicians for accurate decision making of possible diseases and treatments. Subclinical disease has no recognizable clinical findings, thus it is desirable to segment the dental X-Ray image into groups and then use soft computing methods to check the possibility of whether or not any disease occurs therein. Methods The current work proposed a novel framework called Dental Diagnosis System (DDS) for dental diagnosis based on the hybrid approach of segmentation, classification and decision making. It utilized the best dental image segmentation method based on semi-supervised fuzzy clustering for the segmentation task. A new graph-based clustering algorithm called APC+ for the classification task was proposed. A new decision making procedure was designed to determine the final disease from a group of diseases found from the segments. Results The proposed DDS was modeled under the real dental case of Hanoi Medical University, Vietnam including 87 dental images of five popular diseases, namely: root fracture, incluse teeth, decay, missing teeth, and resorption of periodontal bone. The DDS accuracy is 92.74% which is superior to the other methods namely fuzzy inference system (89.67%), fuzzy k-nearest neighbor (80.05%), prim spanning tree (58.46%), kruskal spanning tree (58.46%), and affinity propagation clustering (90.01%). Conclusion Empirical results established that superior performance of the DDS to other related methods the findings of the achieved results can assist dental clinicians in their professional work.

Detecting the manipulation of digital clinical records in dental practice

IntroductionRadiography provides many advantages in the diagnosis and management of dental conditions. However, dental X-ray images may be subject to manipulation with malicious intent using easily accessible computer software. MethodsIn this study, we sought to evaluate a dentist's ability to identify a manipulated dental X-ray images, when compared with the original, using a variant of the methodology described by Visser and Kruger. Sixty-six dentists were invited to participate and evaluate 20 intraoral dental X-ray images, 10 originals and 10 modified, manipulated using Adobe Photoshop to simulate fillings, root canal treatments, etc. ResultsParticipating dentists were correct in identifying the manipulated image in 56% of cases, 6% higher than by chance and 10% more than in the study by Visser and Kruger. ConclusionMalicious changes to dental X-ray images may go unnoticed even by experienced dentists. Professionals must be aware of the legal consequences of such changes. A system of detection/validation should be created for radiographic images.

Image Processing of Panoramic Dental X-Ray for Identifying Proximal Caries

This study aims to facilitate the identification of proximal caries in the Panoramic Dental X-Ray image. Twenty-seven X-Ray images of proximal caries were elaborated. The images in digital form were processed using Matlab and Multiple Morphological Gradient. The process produced sharper images and clarifies the edges of the objects in the images. This makes the characteristics of the proximal caries and the caries severity can be identified precisely.

Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints

In this paper, we propose a novel semi-supervised fuzzy clustering algorithm with spatial constraints for dental segmentation from X-ray images. The detailed contributions include: i) Formulating the spatial features of a dental X-ray image in a dental feature database; ii) Modeling the dental segmentation problem in the form of semi-supervised fuzzy clustering with spatial constraints; iii) Solving the model by the Lagrange multiplier method; iv) Determining the additional information for clustering process by mixing optimal results of Fuzzy C-Means with spatial constraints; v) Proposing a novel Semi-Supervised Fuzzy Clustering algorithm with Spatial Constraints (SSFC-SC) that combines those processes for dental segmentation. The new algorithm is validated on a real dataset from Hanoi Medical University, Vietnam including 56 dental images. The experimental results reveal that the proposed work has better accuracy than the original semi-supervised fuzzy clustering and other relevant methods. We also suggest the most appropriate values of parameters that should be opted for the algorithm.

Dental Diagnosis from X-Ray Images using Fuzzy Rule-Based Systems

In practical dentistry, dentists use their experience to examine dental X-ray images and to derive symptoms from patients for concluding possible diseases. This method is based solely on the own dentists' experience. Dental diagnosis from X-Ray images is proposed to support for dentists in their decision making. This paper presents an application of consultant system for dental diagnosis from X-Ray images based on fuzzy rule. Fuzzy rule was applied in many applications and has important role in computational intelligence, data mining, machine learning, etc. Based on a dental X-ray image dataset, we use Fuzzy C-Means to classify them into clusters and construct the rule set. Fuzzy Inference System is then used to evaluate the rules by three validity indices. These rules accompanied with symptoms from patients help dentists in diagnosing dental diseases. This method is implemented and experimentally validated on the real dataset of Hanoi Medical University Hospital, Vietnam against the related algorithms.

A Tileable CMOS X-Ray Line Detector Using Time-Delay-Integration With Pseudomultisampling for Large-Sized Dental X-Ray Imaging Systems

This paper presents a low-power and low-noise tileable CMOS X-ray line detector with time-delay-integration (TDI) for large-sized dental X-ray imaging systems. The proposed X-ray line detector with TDI adopts pseudomultisampling (PMS) using shifted clocks for the single-slope analog-to-digital converter (ADC) to reduce the required clock cycles for the ADC. The reduced clock cycle decreases the dynamic power and power fluctuation to ensure power stability for large-sized X-ray detectors. The TDI X-ray line detector with PMS was fabricated using 0.18- $\mu \text{m}$ CMOS image sensor process technology with a stitching process, which has an image format of 1010 $\times $ 86 and a pixel size of $75\,\,\mu \text{m}\,\,\times 75\,\,\mu \text{m}$ . The X-ray line detectors are tiled with $1 \times 2$ and $1\,\,\times3$ arrays to enlarge the active areas for dental panoramic and cephalometric X-ray imaging systems, respectively. The measurement results show the random noise of 0.43 LSB and a dynamic range of 78.5 dB, when the number of TDI stages is 86 and the number of PMS steps is 4. The dynamic current and power consumption per ADC are reduced from 157.4 $\mu \text{A}$ and $283.32~\mu \text{W}$ to $41.2~\mu \text{A}$ and 74.16 $\mu \text{W}$ , respectively, when PMS is adopted for TDI.

Aging of imaging properties of a CMOS flat-panel detector for dental cone-beam computed tomography

We have experimentally investigated the long-term stability of imaging properties of a flat-panel detector in conditions used for dental x-ray imaging. The detector consists of a CsI:Tl layer and CMOS photodiode pixel arrays. Aging simulations were carried out using an 80-kVp x-ray beam at an air-kerma rate of approximately 5 mGy s−1 at the entrance surface of the detector with a total air kerma of up to 0.6 kGy. Dark and flood-field images were periodically obtained during irradiation, and the mean signal and noise levels were evaluated for each image. We also evaluated the modulation-transfer function (MTF), noise-power spectrum (NPS), and detective quantum efficiency (DQE). The aging simulation showed a decrease in both the signal and noise of the gain-offset-corrected images, but there was negligible change in the signal-to-noise performance as a function of the accumulated dose. The gain-offset correction for analyzing images resulted in negligible changes in MTF, NPS, and DQE results over the total dose. Continuous x-ray exposure to a detector can cause degradation in the physical performance factors such the detector sensitivity, but linear analysis of the gain-offset-corrected images can assure integrity of the imaging properties of a detector during its lifetime.

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.

Boosting Proximal Dental Caries Detection via Combination of Variational Methods and Convolutional Neural Network

Proximal dental caries are diagnosed using dental X-ray images. Unfortunately, the diagnosis of proximal dental caries is often stifled due to the poor quality of dental X-ray images. Therefore, we propose an automatic detection system to detect proximal dental caries in periapical images for the first time. The system comprises four modules: horizontal alignment of pictured teeth, probability map generation, crown extraction, and refinement. We first align the pictured teeth horizontally as a pre-process to minimize performance degradation due to rotation. Next, a fully convolutional network are used to produce a caries probability map while crown regions are extracted based on optimization schemes and an edge-based level set method. In the refinement module, the caries probability map is refined by the distance probability modeled by crown regions since caries are located near tooth surfaces. Also we adopt non-maximum suppression to improve the detection performance. Experiments on various periapical images reveal that the proposed system using a convolutional neural network (CNN) and crown extraction is superior to the system using a naive CNN.

Edge Detection and Features Extraction for Dental X-Ray

Recently, the dental X-Ray images have been used in different applications, particularly in the forensic field. The researchers focuses on the separation of the underlying teeth individually to obtain their features. These features can be utilized as a key solution for the identifications. In this paper, an edge detection of the involved teeth is proposed using a three stages MATLAB algorithm based on different methods such as, CLAHE, Canny, Otsu's, and 8-Connectivity. In addition, the proposed algorithm extracts the features of the investigated teeth as an exported file. These features are Standard Deviation (STD), Euler number and Area which are extracted from the bite-wing images. The stages of the proposed algorithm are image segmentation, classification and features extraction. It is important to note that the missing teeth has been considered in case of appearance. The missing teeth are assumed to be a separated objects. This is to overtake the problem of missing teeth after registering the original ones in the stored database used for identifications. The obtained results show the clear outperformance of the proposed algorithm in terms of edge detection and features’ extraction. The missing teeth in an image are tested and the achieved results presents the detection and features of such teeth dramatically. The proposed system is implemented and tested in the MATLAB software environment using a personal computer of a Core(TM) i7 processor and 6 GB RAM over a 64-Windows 10 operating system.

Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation

Abstract Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted. This paper proposes a new system for automated dental X-ray identification. The scheme extracts tooth and dental work contours from the X-ray images and uses the Hausdorff-distance measure for ranking persons. This combination of state-of-the-art approaches with a novel lowest cost path-based method for separating a dental X-ray image into individual teeth, is able to achieve comparable and better results than what is available in the literature. The proposed scheme is fully functional and is used to accurately identify people within a real dental database. The system is able to perfectly separate 88.7% of the teeth in the test set. Further, in the verification process, the system ranks the correct person in top in 86% of the cases, and among the top five in an astonishing 94% of the cases. The approach has compelling potential to significantly reduce the time spent on dental identification.

Classification of teeth in cone-beam CT using deep convolutional neural network

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification.

Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images.

Medical diagnosis is considered as an important step in dentistry treatment which assists clinicians to give their decision about diseases of a patient. It has been affirmed that the accuracy of medical diagnosis, which is much influenced by the clinicians' experience and knowledge, plays an important role to effective treatment therapies. In this paper, we propose a novel decision making method based on fuzzy aggregation operators for medical diagnosis from dental X-Ray images. It firstly divides a dental X-Ray image into some segments and identified equivalent diseases by a classification method called Affinity Propagation Clustering (APC+). Lastly, the most potential disease is found using fuzzy aggregation operators. The experimental validation on real dental datasets of Hanoi Medical University Hospital, Vietnam showed the superiority of the proposed method against the relevant ones in terms of accuracy.

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.

Digital Dental X-ray Database for Caries Screening

Standard database is the essential requirement to compare the performance of image analysis techniques. Hence the main issue in dental image analysis is the lack of available image database which is provided in this paper. Periapical dental X-ray images which are suitable for any analysis and approved by many dental experts are collected. This type of dental radiograph imaging is common and inexpensive, which is normally used for dental disease diagnosis and abnormalities detection. Database contains 120 various Periapical X-ray images from top to bottom jaw. Dental digital database is constructed to provide the source for researchers to use and compare the image analysis techniques and improve or manipulate the performance of each technique.

Identification of the Proximal Caries of Dental X-Ray Image with Multiple Morphology Gradient Method

This study aims to perform the sharpening of the dental x-ray image in the form of a panoramic dental x-ray. The method used in this study was segmentation morphology consisting of the dilation, erosion and gradient process. This study also developed a process of morphology gradient of subtracting morphology dilation results with the results of morphology erosion dilation in iterating basis. The results achieved indicate that the image enhancement process in each iteration stage can display caries objects clearly, making it easier to identify proximal caries. In this study have been compiled a looping morphology gradient algorithm which is called multiple morphology gradient

A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.

Dynamic semi-supervised fuzzy clustering for dental X-ray image segmentation: an analysis on the additional function

Dental X-ray image segmentation is a necessary and important process in medical diagnosis, which assists clinicians to make decisions about possible dental diseases of a patient from a dental X-ray image. It is a multi-objective optimization problem which involves basic components of fuzzy clustering, spatial structures of a dental image, and additional information of experts expressed through a pre-defined membership matrix. In our previous work, the authors presented a semi-supervised fuzzy clustering algorithm using interactive fuzzy satisficing named as SSFC-FS for this problem. An important issue of SSFC-FS is that the pre-defined membership matrix is a fixed function in the sense that it uses the same structure and parameters for all dental images. This is a shortcoming of SSFC-FS since each image has its own structure and morphology so that it needs different membership matrices. In this paper, the authors propose another new dynamic semi-supervised fuzzy clustering called SSFC-FSAI that extends SSFC-FS by employing a collection of pre-defined membership matrices for dental images. A procedure to choose a suitable pre-defined membership matrix for a given dental X-ray image is proposed and attached to SSFC-FSAI. Experimental results on a real dataset of 56 dental X-ray images from Hanoi University of Medical in 2014 - 2015 show that SSFC-FSAI has better accuracy than SSFC-FS and the relevant algorithms.

Evaluation of Root Canal Obturation by Micro-computed Tomography for Endodontic Training in Dental Students

Introduction: The purpose of this study was to evaluate the outcomes of root canal preparation and obturation by third year students who were performing root canal treatment for the first time, with micro-computed tomography (micro-CT) and compare the images taken at the first and second obturations for their training. Methods: Single-rooted straight artificial right maxillary incisors for endodontic training were used for root canal preparation. The canals were obturated with gutta-percha and sealer. Six incisors judged to be well-obturated based on 2-D dental X-ray images were selected for micro-CT scanning. Based on the micro-CT images of the obturation performed for the first time, the areas for improvement were explained to each student individually. Then, root canal preparation and obturation were repeated using a new artificial tooth. The obturated artificial tooth was scanned by micro-CT. Digital three-dimensional (3-D) images were constructed. The volumes of the gaps and voids in the root canal and preparation size were calculated from the cementoenamel junction up to the apex after first and second root canal obturation. Results: After first root canal obturation, mean value of the gaps and voids was reduced. Significant differences were observed between the first time and second time obturation group (p=0.05). The mean value of the preparation size was slightly increased. No significant changes in the preparation size were observed. Conclusions: These results suggested that micro-CT was an effective tool for evaluation of the outcome of endodontic training.

A Novel Framework Using Graph-Based Clustering for Dental X-Ray Image Search in Medical Diagnosis

A Novel Framework Using Graph-Based Clustering for Dental X-Ray Image Search in Medical Diagnosis