Automatic dental caries detection in bitewing radiographs.

Viriri, A Survey of Dental Caries Segmentation and Detection Techniques", Computational and Mathematical Methods in Medicine, (2022). Conventional methods have been limited in performance due to the complex visual characteristics of dental caries images, which consist of hidden or inaccessible lesions. A the majority of dental caries in these communities go untreated, and severe cases can impair quality of life.

Tooth Anatomy

Dental Caries

Incipient caries are caries that have a depth of less than half of the tooth enamel. Moderate caries is caries that is more than halfway through the enamel but does not touch the bone. Severe caries is caries that extends more than halfway through the dentin and even reaches the pulp.

Dental radiographs-X-rays

Factors affecting Dental Caries

[18] further explain how measuring salivary flow is an important measure for risk assessment and dental caries management.

Prevention of Caries

Diagnosis of Caries

Treatment of Caries

Computer-aided diagnosis (CAD) systems have become an important tool in medical radiology and are widely used in the detection of various diseases. Further, after detecting possible caries, the CAD system tries to reduce or rather eliminate the false positives detected by the previous stage.

Motivation

Early recognition and automatic detection of dental caries helps to reduce the infection rate and even prevent further progression of the lesions. The new approach significantly helps in detecting and locating dental caries compared to existing models.

Problem Statement

Research Objectives

Contributions

Filters are then used to remove noise from the images and preserve their edge boundaries. Then, the spot detection and connectivity clustering model are used to detect and isolate the caries candidates, respectively. Finally, a threshold value is used to select caries candidates and to eliminate false positives from detected caries candidates.

Thesis Outline

Therefore, X-ray images can be accurately segmented while preserving the important details necessary for dental caries detection. The second part looks at thresholding and connected component analysis methods used to isolate teeth from their background pixels and from each other in X-rays. This chapter covers the final stage of the caries detection method and details methods used to isolate potential caries and evaluate them.

Introduction

Summary

In this article, we present an overview of deep learning methods used or related to segmentation and detection of dental caries. Furthermore, we summarized the performance of evaluation protocols used in deep learning dental segmentation and detection. Therefore, the choice of the segmentation method to use depends on similarities and discontinuities of regions of interest on an image.

Additionally, the choice of detection method to use depends on the quality, pixels, and color intensity characteristics of an image.

Introduction

Finally, segmentation is done by thresholding, and connected components are identified to extract the region of interest (ROI) of the teeth. Early detection of dental caries lesions is an important determinant of treatment measures and is therefore a beneficiary of the intro-. Analysis of dental radiographs requires some processing of the images to obtain useful information.

Most of the deep learning network methods have achieved great success in segmenting medical images. Soft computing techniques proposed in [18] are used to improve performance of watermarking algorithms and their applications. There is also the use of the Otsu threshold and connected component analysis proposed in [20] for the segmentation of periapical dental images to aid automated forensic identification.

Connected component analysis is used to extract the region of interest (ROI) of the segmented teeth. Due to the nature of dental images, there is a need for hybrid datasets to help the networks perform well. Due to the nature of dental images, there is a need for hybrid datasets to help the networks perform well.

Simon, “Segmentation of dental radiographic images,” in Proceedings of the Third International Conference on Advanced Informatics for Computational Research, p.

Summary

Introduction

Early detection of dental caries is an important determinant for treatment and benefits greatly from the introduction of new tools such as dental radiography. In this paper, we propose a deep learning-based technique for dental caries detection, namely: spot detection. Early detection of dental caries lesions is an important determinant of treatment and, therefore, a beneficiary of the introduction of new tools [3].

Most dentists use bitewing X-rays to help find the location of dental caries. Most people at risk for dental caries are low-income minorities, socially and economically disadvantaged and uneducated people. Accordingly, our approach presents a technique that evaluates the effectiveness of deep learning methods for the detection of dental caries in bite-wing X-rays.

We introduce the use of blob detection on bitewing radiographs to detect dental caries. Furthermore, performance evaluation of the proposed approach was based on its ability to detect and localize dental caries on test data images. Figure 5 shows the identification of dental caries locations remaining after applying a convexity threshold of 1.0 that eliminates false positives.

Images of dental caries detected after removing false positives using a convexity threshold.

Summary

Performance evaluation of the overall method proposed in this thesis is based on the analysis and success rates of both the segmentation and caries diagnosis processes. It is also important to understand the nature of the data set being analyzed to achieve these results. Results from different existing segmentation methods are readily available, and are therefore compared with those of the proposed method.

To determine the performance of a caries diagnostic method, comparisons must be made with appropriate detection rates of the ability of existing systems to diagnose dental caries. The performance of the proposed method was measured by its ability to locate known caries, and any predictions made regarding caries not previously identified were considered false positives.

Dataset

Caries Detection Framework

Segmentation Results

Separation of teeth is considered correct if the separation did not cause division of the teeth. Teeth that were already partial due to being at the edge of the radiograph were considered correctly separated if no further bias was caused. Teeth that were not segmented correctly were either due to poor image contrast, where enhancement techniques could not rescue a distinction between teeth and non-tooth structures.

Dropout is simply handing over devices in the network that are not needed for the processing stage. It should be noted that the proposed segmentation method after optimization showed better results compared to other existing implementations.

Caries Detection Results

A comparison of the results was made with various other diagnostic methods of caries detection, to determine whether the results fell within acceptable limits or not. The proposed method achieves better accuracy results than others with its presentation of spot detection in dental radiographs. Although these results fall within the acceptable range of a new approach, [21] enjoys slightly higher results and this is attributed to different reasons.

Another reason is attributed to errors that occur in the analysis of bitewing X-ray images, namely: lighting errors and image magnification anomalies.

Conclusion

We discussed the caries detection framework to give an overview of all the preliminary processes combined to achieve the final results. The results showed that the proposed framework performs well compared to existing methods. The next chapter will delve into the implications of the results discussed in this chapter and provide more in-depth conclusions.

There is great potential for the use of dental radiography, and in particular work focused on caries detection. Most of the existing systems focus a lot on segmentation of caries and not on caries detection. Finally, the diagnostic method was handled by Gaussian filters for noise reduction, blob detection and convexity threshold for caries detection.

The new method showed improved performance due to the combined aspect of thresholding to separate individual teeth, while the active contour model locates the marginal boundaries. The proposed caries detection method gave favorable results compared to other existing detection methods with a correct diagnosis rate of 97%. Therefore, we can conclude that the proposed framework described in this thesis provides a new approach to both tooth segmentation problems and dental caries detection.

The success rate of the proposed caries detection method on a supervised model provides a possible avenue for future work using unsupervised models, thus leading to even better results.

Future Work

The dataset had few images for processing and there was a need to add more images through various processing methods, which will help deep learning by the model. There are a number of encouraging future perspectives of study that could see improvement in dental segmentation and detection of dental caries. There is a need for publicly available datasets for dental images to enable deep learning.

Deep learning methods and networks can also be improved by introducing weight regularization to improve their performance. Deep learning methods can be further improved by combining different models or techniques into hybrid models that will ultimately improve the overall evaluation performance. The combination can take place at any stage of the model, for example by combining two or more pre-processing techniques to arrive at a single step to improve image quality.

In addition to this optimization, future work will look at expanding the proposed system's ability to diagnose different images, as it is currently used to test only bitewing radiographs from the same data set. A comparison of the diagnostic accuracy of bitewing, periapical, unfiltered and filtered digital panoramic images for approximate caries detection in posterior teeth. Mottled teeth: an endemic developmental imperfection of the enamel of the teeth hitherto unknown in the literature of dentistry.

In 2017 IEEE Engineering in Medicine and Biology Society (EMBC) 39th Annual International Conference, Pages 1998-2001.

Cross-section of the molar tooth [35]

Periapical radiograph, Bitewing radiograph, Panoramic radiograph re-

Computer aided system diagram as by [28]

Images in the original dataset, images in the generated dataset after

An overview of the caries detection framework

Examples of the segmentation results in the dataset

Examples of Gaussian filtered images

Examples of all detected caries blobs