New Dental Image Enhancement Network Proposed for Diagnosis of Oral and Dental Diseases

A group of researchers from China and Pakistan have proposed a new dental image enhancement strategy that could help improve the diagnosis of oral and dental diseases. Traditional enhancement methods and network-based methods have limited adaptability towards varying conditions, and they often fail to handle non-uniform brightness and low contrast in captured images. 

Paired Branch Denticle-Edification Network

To address this, the researchers have developed a proposed network, called the paired branch Denticle-Edification network (Ded-Net). This decomposes input dental images into reflection and illumination in a multilayer Denticle network (De-Net), and performs subsequent enhancement operations to remove the hidden degradation of reflection and illumination. 

The adaptive illumination consistency is maintained through the Edification network (Ed-Net), and the network is regularized following the decomposition congruity of the input data. The proposed method has been shown to improve visibility and contrast while preserving the edges and boundaries of the low-contrast input images. It could prove useful for intelligent and expert system applications for future dental imaging.

Early Detection of Oral and Dental Diseases

Dental image analysis is crucial for detecting and diagnosing oral and dental diseases. These diseases often result from lifestyle factors and can appear irrespective of age, caste, creed, sex, and location. Early detection provides a better assessment for diagnosis, and it limits the overall cost and complications. However, images captured with digital devices for preliminary diagnosis encounter low contrast and give rise to new challenges. 

Effective dental lesion detection technologies can determine the incipient carious lesion with the help of effective changes in the tooth surface. Moreover, the improvement in the visual quality of the captured images can assist significantly in improving associated tasks such as segmentation, computer-assisted oral and maxillofacial surgeries, and many image-guided robotics and intelligent expert system application tasks.

In general practice, intraoral X-rays are often used, but they provide a limited angle for the view of vision and expose sensitive facial parts to ionizing radiation, which has several disadvantages. Spectral imaging, which uses a light ring and a mobile spectral camera, can record the reflection spectrum of the sample. However, it limits the illumination intensity and reflection reaching the camera and raises the acquisition time, resulting in over-exposure and under-exposures.

New Adaptive Solutions Required

The existing contrast enhancement methods for medical images, such as histogram equalization (HE), classic solutions, deep learning-based solutions, and feature enhancement based on illumination weighting (FEW), rely on image pairs or large scale datasets, which provide a fixed balance of contrast with limited adaptability towards nascent conditions. 

Therefore, new adaptive solutions are required to resolve these challenging problems to improve the performance of practical medical applications. The recent decomposition-based approaches, deep retinex, kindling the darkness method (KinD), and beyond brightening the low light images (BBLLI) method, are fundamentally dependent on a large scale dataset of image pairs.

The proposed method is adaptive and has the freedom of adaptability towards desired contrast levels, making it suitable for practical dental imaging. It could significantly contribute to improving the performance of many deep learning-based methods, including the improvement in the performance of the visual tracking and surgical robots. 

Additionally, enhancement techniques help physicians for better assessment and early diagnosis, which saves additional complications and overall costs. Thus, more adaptive and freestyle enhancement techniques are imperative to improve the performance of associated tasks.