Dental Image Segmentation Research Articles

Dental bitewing radiographs segmentation using deep learning-based convolutional neural network algorithms.

Dental radiographs, particularly bitewing radiographs, are widely used in dental diagnosis and treatment Dentalimage segmentation is difficult for various reasons, such as intricate structures, low contrast, noise, roughness, andunclear borders, resulting in poor image quality. Recent developments in deep learning models have improvedperformance in analyzing dental images. In this research, our primary objective is to determine the most effectivesegmentation technique for bitewing radiographs based on different metrics: accuracy, training time, and thenumber of training parameters as a reflection of architectural cost. In this research, we employ several deep learning models, namely Resnet-18, Resnet-50, Xception, InceptionResnet v2, and Mobilenetv2, to segment bitewing radiographs. The process begins by importing the radiographsinto MATLAB®(MathWorks Inc), where the images are first improved, then segmented using the graph cut methodbased on regions to produce a binary mask that distinguishes the background from the original X-ray. The deep learning models were trained on 298 and 99 radiograph training and validation sets and were evaluatedusing 99 images from the testing set. We also compare the segmentation model using several criteria, includingaccuracy, speed, and size, to determine which network is superior. Furthermore, we compare our findings with priorresearch to provide a comprehensive understanding of the advancements made in dental image segmentation. Theaccurate segmentation achieved was 93.67% and 94.42% by the Resnet-18 and Resnet-50 models, respectively. This research advances dental image analysis and facilitates more accurate diagnoses and treatment planning bydetermining the best segmentation technique. The outcomes of this study can guide researchers and practitionersin selecting appropriate segmentation methods for practical dental image analysis.

Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images.

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

AbstractDentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.

Composite Feature Set Based Dental Image Segmentation Framework through Unsupervised Learning

Composite Feature Set Based Dental Image Segmentation Framework through Unsupervised Learning

Flower pollination based dental image segmentation

ABSTRACT Flower pollination-based algorithm (FPA), inspired from the pollination process of flowers, is a population-based metaheuristic optimisation algorithm for solving real-world problems. This article introduces the feature of insectivorous plants in catching the pollinators (preys) with the solution procedure of FPA for avoiding sub-optimal traps and models the dental image segmentation problem as an optimisation problem. This enhanced FPA (EFPA) is then applied in solving the formulated multilevel segmentation problem of dental images. The proposed EFPA-based method optimises the threshold values for dental images by effectively exploring the problem space. The segmented images along with PSNR values of the proposed method are compared with FPA-based and OTSU-basedsegmentation methods for exhibiting its superior performance.

The local ternary pattern encoder–decoder neural network for dental image segmentation

Recent advances in medical imaging analyses, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the centre of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performances in the analysis of medical applications and systems. Deep learning techniques have achieved improved performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps dentists to diagnose dental caries. However, the performance of the deep networks used for these analyses are restrained by various challenging features found in dental carious lesions. Segmentation of dental images is often difficult due to the vast variety of types of topology, intricacies of medical structure and poor image quality caused by conditions such as low contrast, noise, irregular, and fuzzy border edges. These issues are exacerbated by low numbers of data images available for any particular analysis. A robust local ternary pattern encoder–decoder network (LTPEDN) is proposed to overcome dental image segmentation challenges and minimise the computational resources required. This new architecture is a modification of existing methods using an LTP. Images are preprocessed via augmentation and normalisation techniques to increase and prepare the datasets. Thereafter, the dataset input is sent to the LTPEDN for training and testing the model. Segmentation is performed using the non-learnable layers (the LTP layers) and the learnable layers (standard convolution layers), to extract the ROI of the teeth. The method was evaluated on an augmented dataset of 11, 000 dental images. It was trained on 8, 800 training set images and tested on 2, 200 testing set images. The new method is shown to be 94.32% accurate.

Descriptive analysis of dental X-ray images using various practical methods: A review.

In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.

Dental Image Segmentation and Classification Using Inception Resnetv2

The automated process for dental caries detection draws increasing attention with the technological innovation in machine learning methods. This is a core issue in dental diseases especially in the detection of caries as it leads to serious health ailments. This paper takes an effort to adequately segment and identify dental diseases. There are four main steps. The preprocessing technique uses binary histogram equalization which increases the texture region visibility for the caries detected on dental images. The novel technique of segmentation with Curvilinear Semantic Deep Convolutional Neural Network (CSDCNN) is proposed in this paper . The segmentation is followed by the proposed Inception resnetV2, which acts as the classification technique to determine the caries in dental images. The proposed segmentation algorithm is used to determine a dental degree of membership. The inception is brought out with different scales of information, which relates to various input images as data. An examination of the x-ray images will detect the impact of illness on a tooth. Particularly for the segmentation and classification mission, we deemed four diseases: dental caries, periapical infection, periodontal, and pericoronal diseases. Based on the number of input functional parameters, the Inception resnetV2 classifies different image categories effectively. The proposed Inception resnetV2 has become the most effective tool in machine learning to solve problems like image classification with a high order of accuracy. The average accuracy of the device proposed is 94.51%. This provides higher classification accuracy when compared to other existing methods.

Dental Images' Segmentation Using Threshold Connected Component Analysis.

Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to diagnose dental caries. The performance of these deep networks is however restrained by various challenging features of dental carious lesions. Segmentation of dental images becomes difficult due to a vast variety in topologies, intricacies of medical structures, and poor image qualities caused by conditions such as low contrast, noise, irregular, and fuzzy edges borders, which result in unsuccessful segmentation. The dental segmentation method used is based on thresholding and connected component analysis. Images are preprocessed using the Gaussian blur filter to remove noise and corrupted pixels. Images are then enhanced using erosion and dilation morphology operations. Finally, segmentation is done through thresholding, and connected components are identified to extract the Region of Interest (ROI) of the teeth. The method was evaluated on an augmented dataset of 11,114 dental images. It was trained with 10 090 training set images and tested on 1024 testing set images. The proposed method gave results of 93% for both precision and recall values, respectively.

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.

INITIAL CONTOUR GENERATION APPROACH IN LEVEL SET METHODS FOR DENTAL IMAGE SEGMENTATION

Segmentation is challenging process in medical images especially on dental x-ray images. Level set methods have best result on medical and dental image segmentation. Initial Contour (IC) is the essential step in level set methods to initialize the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and produce accurate result. In this paper a new region-based technique for IC generation, is proposed to generate the most suitable IC. We have utilized the statistical and morphological information inside and outside the contour to establish a region-based map function. This function is able to find the suitable IC on images to perform by level set methods. Experiments on dental x-ray images demonstrate the robustness of segmentation process using proposed method even on noisy images and with weak boundary. Furthermore, computational cost of segmentation process is reduced.

Level Set and Morphological Operation Techniques in Application of Dental Image Segmentation

Level Set and Morphological Operation Techniques in Application of Dental Image Segmentation

DISR: Dental image segmentation and retrieval

In this paper, we propose novel algorithms for retrieving dental images from databases by their contents. Based on special information of dental images, for better content-based dental image retrieval and representation, the image attributes are used. We propose Dental Image Segmentation and Retrieval (DISR), a content-based image retrieval method that is robust to translation and scaling of the objects in the images. A novel model is used to calculate the features of the image. We implemented the dentition plaster casts and proposed a special technique for segmenting teeth in our dental study models. For testing the efficiency of the presented algorithm, a software system is developed and 60 dental study models are used. The models are covering different kinds of malocclusions. Our experiments show that 95% of the extracted results are accurate and the presented algorithm is efficient.