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

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.

Semi-supervised clustering methods are applied in various fields. However, these methods have the limitations when the size of data is large. Thus, distributed clustering models are proposed. By separating data set into some parts, distributed models overcome the challenges of big size dataset. In this paper, a novel model, based on semi-supervised fuzzy clustering and distributed mechanism, is proposed. In our research, the method to define distributed additional information is introduced. This additional information is used to implement distributed semi-supervised fuzzy clustering model on three datasets. The main contribution in this paper is the proposal of distributed semi-supervised fuzzy clustering algorithm (denoted as DSSFCM) that performs on Master – Slave model on homogeneous datasets.

The impulse noise often damages the human dental X-Ray images, leading to improper dental diagnosis. Hence, impulse noise removal in dental images is essential for a better subjective evaluation of human teeth. The existing denoising methods suffer from less restoration performance and less capacity to handle massive noise levels. This method suggests a novel denoising scheme called "Noise removal using Partition supported Median, Interpolation, and Discrete Wavelet Transform (NRPMID)" to address these issues. To effectively reduce the salt and pepper noise up to a range of 98.3 percent noise corruption, this method is applied over the surface of dental X-ray images based on techniques like mean filter, median filter, Bi-linear interpolation, Bi-Cubic interpolation, Lanczos interpolation, and Discrete Wavelet Transform (DWT). In terms of PSNR, IEF, and other metrics, the proposed noise removal algorithm greatly enhances the quality of dental X-ray images.

In dental X-ray images, an accurate detection of cephalometric landmarks plays an important role in clinical diagnosis, treatment and surgical decisions for dental problems. In this work, we propose an end-to-end deep learning system for cephalometric landmark detection in dental X-ray images, using convolutional neural networks (CNN). For detecting 19 cephalometric landmarks in dental X-ray images, we develop a detection system using CNN-based coordinate-wise regression systems. By viewing x- and y-coordinates of all landmarks as 38 independent variables, multiple CNN-based regression systems are constructed to predict the coordinate variables from input X-ray images. First, each coordinate variable is normalized by the length of either height or width of an image. For each normalized coordinate variable, a CNN-based regression system is trained on training images and corresponding coordinate variable, which is a variable to be regressed. We train 38 regression systems with the same CNN structure on coordinate variables, respectively. Finally, we compute 38 coordinate variables with these trained systems from unseen images and extract 19 landmarks by pairing the regressed coordinates. In experiments, the public database from the Grand Challenges in Dental X-ray Image Analysis in ISBI 2015 was used and the proposed system showed promising performance by successfully locating the cephalometric landmarks within considerable margins from the ground truths.

Deep Convolution Neural Network is one of the most powerful tools to solve complex problems of image classification, image recognition, financial analysis, medical diagnosis and many similar problems. A dental panoramic image consists of collection of teeth of both upper jaw and lower jaw. Automatic classification of dental panoramic images into various tooth types such as canines, incisors, premolars and molars has been a challenging task and involves crucial role of an experienced dentist. In this paper, we propose a technique for numbering and classification of the panoramic dental images. The proposed algorithm consists of four stages namely pre-processing, segmentation, numbering and classification. The pre-processed panoramic dental images are segmented using fuzzy c-mean clustering and subjected to vertical integral projection to extract a single tooth. The image dataset consists of 400 dental panoramic images collected from various dental clinics. The 400 dental images are divided into 240 training samples and 160 testing samples. The image data set is augmented by applying various transformations. Panoramic dental images are further numbered using a universal dental numbering system. Finally, the classification is done with the help of 6-layer deep convolution neural network (DCNN) consisting of 3 convolutional neural network and 3 fully connected network. The tooth is classified as canine, incisor, molar and premolar. An accuracy of 95% has been achieved for augmented database and 92% for original dataset with the proposed algorithm. The proposed numbering and classification of dental panoramic images is useful in biomedical application and postmortem recording of dental records. In case of big calamity, the system can also assist the dentist in recording post mortem dental record that is a very lengthy and arduous task.

Novel approaches to determine age and gender from dental x-ray images by using multiplayer perceptron neural networks and image processing techniques

Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement

The subject of research in the article is the effectiveness of adaptive methods of preprocessing of medical images, in particular adaptive bilateral filter and modified CLAHE, in the tasks of segmentation of dental X-ray images. These methods make it possible to preserve important image details and effectively reduce noise, even in cases of high variability of images coming from different sources. The goal of the work is to study the impact of adaptive preprocessing methods on increasing the accuracy of segmentation of medical images and to determine the optimal combination of methods that provide the best results in segmentation tasks. The article addresses the following tasks: experimental comparison of adaptive preprocessing methods with traditional approaches, evaluation of segmentation efficiency using metrics such as Dice Score, Jacquard Coefficient (IoU Score), Precision and Sensitivity/Completeness (Recall)), as well as analysis of the effect of pre-processing on the quality of segmentation. The following methods are used: mathematical modeling, neural network training based on the U-Net model with a pre-trained timm-resnest101e encoder, image scaling to 512x512 pixels, training with a fixed learning rate of 0.001. The following results were obtained: the combined use of the adaptive bilateral filter and the modified CLAHE provided the highest segmentation quality indicators (Dice Score 0.9603 and Jacquard Coefficient (IoU Score) 0.94501), surpassing traditional methods. This proves the advantage of adaptive approaches in preserving the contours of objects and reducing noise. Conclusions: the application of adaptive preprocessing methods significantly improves the accuracy of segmentation of medical images. The combined approach including the adaptive bilateral filter and the modified CLAHE is the most effective for medical imaging tasks, which increases the accuracy of diagnosis and the reliability of automated decision support systems.

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

Dental X-ray picture segmentation assists tooth diagnosis. Too much chocolate and unhealthy diets have increased tooth diseases in recent decades. Dental radiography supports clinical diagnosis, treatment, and quality assessment. Clinical quality has been improved by digitalising dental X-ray image analysis systems. This inspires an early-detection dental disease prediction model. Dental X-ray image segmentation for disease diagnosis is gaining attention. Deep learning has grown in various image processing domains. Image segmentation is a key field of computer vision research. The U-Net, a popular picture segmentation method, has been widely applied to medicine. Here, a unique deep learning system, the UNet model improved with ANN, notably MLP, segments dental pictures better. An ANN-enhanced U-Net split the X-ray pictures, and a GLCM selected the features. CNN-mobile net classifier. Four metrics - global accuracy, mean accuracy, mean IoU, weighted IoU, and MeanBFScore - and classification with precision, recall, Fscore, specificity, and accuracy evaluate segmentation methodology. Our method is accurate and outperforms others in all segmentation and classification tasks.

The evolution in the field of medical image analysis is rapidly increasing, particularly in challenging tasks such as disease detection, segmenting organs, and abnormalities prediction. When it comes to diagnosis of dental diseases with the naked eye image segmentation plays a crucial role in addressing the difficulties faced by dentists worldwide. This paper introduces a novel method for smart dental image segmentation by leveraging Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and the U-net architecture. The process begins with thorough data preprocessing, employing techniques such as noise reduction and image enhancement to ensure dataset quality and consistency. Following this, individual CNN, GAN, and U-net models are developed and trained. CNNs extract features from dental images, GANs generate realistic images for training, and U-net performs the segmentation task . Deep neural network is employed in the model to obtain the binary mask to improve the segmentation quality of the teeth to statistically identify form and space changes. Detailed descriptions of hardware and software configurations in the experimental setup ensure reproducibility, while key training parameters provide insights into the training process. Results showcase the model's superior performance through quantitative metrics like accuracy, precision, and recall, along with qualitative assessments comparing segmentation results to ground truth annotations. this paper presents a comprehensive approach to dental image segmentation, combining advanced neural network architectures with meticulous data preprocessing and thorough experimental setup. The integration of CNNs, GANs, and U-net models leads to enhanced segmentation accuracy, validated through both quantitative evaluations and qualitative comparisons with ground truth annotations.

Semi-supervised fuzzy clustering with metric learning and entropy regularization

Osteoporosis screening using dental X-Ray images has been growing an interesting research. Existing methods for osteoporosis screening have been performed using the dental periapical or panoramic in X-Ray images. There was limited research using both the periapical and panoramic due to the expensive cost of obtaining data. This paper presents a combination of the periapical and panoramic images for osteoporosis detection. The images processing was performed to obtain the shape-based features of the porous trabecular bone on both the dental radiograph images. The important features were selected from the extracted features. These selected features were chosen for osteoporosis detection using the decision tree. The quantitative evaluation used confusion matrix. It was found accuracy rate to be 73.33%, sensitivity rate to be 72.23, and specificity rate to be 72.23% for data testing.

Dental disease is a significant problem in humans and deep learning is increasingly being used in the field of dentistry. The purpose of this literature review is to identify dental problems such as tooth identification, caries, treated teeth, dental implants, and endodontic treatment using deep learning approaches in dental image analysis which help dentists in their decision-making process. Dental radiographs are essential for the diagnosis and detection of dental issues. The study focuses on the development and use of several image segmentation/ classification algorithms in the extraction of regions of interest from dental radiographs. To predict different forms of impacted teeth, a convolutional neural network is trained, validated, and tested using dental images with labelled images datasets. Our research suggests that Hybrid models such as CNN-SVM, CNN-KNN or CNN-LSTM or K-mean can be trained over mixed data sets to produce excellent results whereas compared to other image segmentation algorithms, UNet architecture performs better at segmenting dental Xray images.

Semi-supervised clustering leverages side information such as pairwise constraints to guide clustering procedures. Despite promising progress, existing semi-supervised clustering approaches overlook the condition of side information being generated sequentially, which is a natural setting arising in numerous real-world applications such as social network and e-commerce system analysis. Given emerged new constraints, classical semi-supervised clustering algorithms need to re-optimize their objectives over all data samples and constraints in availability, which prevents them from efficiently updating the obtained data partitions. To address this challenge, we propose an efficient dynamic semi-supervised clustering framework that casts the clustering problem into a search problem over a feasible convex set, i.e., a convex hull with its extreme points being an ensemble of m data partitions. According to the principle of ensemble clustering, the optimal partition lies in the convex hull, and can thus be uniquely represented by an m-dimensional probability simplex vector. As such, the dynamic semi-supervised clustering problem is simplified to the problem of updating a probability simplex vector subject to the newly received pairwise constraints. We then develop a computationally efficient updating procedure to update the probability simplex vector in O(m2) time, irrespective of the data size n. Our empirical studies on several real-world benchmark datasets show that the proposed algorithm outperforms the state-of-the-art semi-supervised clustering algorithms with visible performance gain and significantly reduced running time.