Noise Reduction In Orthodontic AI: Techniques And Best Practices

Noise in medical imaging is a curse that can waste time and may even lead to mis- or under-diagnosis. Image noise refers to the production of unwanted artifacts and graininess in a medical image that masks important anatomical details.

Numerous environmental factors influence radiographic image quality. Image-capturing system issues and fluctuations in sensor sensitivity (with temperature changes) are major contributors to noise. Expert dentists are trained to spot the various types of noise in a medical image and make efforts to reduce it.

Conventional methods of reducing noise in X-rays, CBCTs, and MRI scans haven’t really sufficed the needs of most clinicians. The problem with most of the prevalent methods is that they blur the images while removing noise. The loss of fine details is costly in the world of orthodontics.

Therefore, artificial intelligence is also used to denoise medical images. To improve diagnostic efficiency, orthodontists can adopt the following effective techniques to reduce noise in medical images:

Techniques To Reduce Noise In Images

Filtering techniques

The filtering technique is one of the most commonly adopted techniques for reducing noise. The anisotropic diffusion filtration technique is a highly prevalent method that effectively reduces noise but tends to blur images. It works by removing unimportant details like:

• Lines
• Edges
• Unimportant artifacts

Studies show that nonlinear anisotropic diffusion can preserve semantic features of an image and thus, favors computerized applications to enhance medical image quality.

Another filtration technique employed to denoise medical images is block-matching with 3D transformation. According to a study, block-matching shows good performance in denoising images but it only works on images with low noise.

Learning-Based Methods

The most advanced method of getting rid of medical image noise is a learning-based technique. Deep learning (DL) is a sub-field of machine learning that helps eliminate image noise. Models have the capacity to detect intricate patterns in large datasets. When exposed to vast sets of noise-free medical imagery, the advanced models effectively discern between noise and true anatomical structures.

AI uses convolutional neural networks (CNNs) and deep learning (DL) techniques to handle noise in orthodontic images. A modern study investigated the two-stage method of removing noise using convolutional neural networks. Unlike the common methods that focus on either image or residual noise, CNN models can utilize both medical image and noise simultaneously. This can lead to better correction of noise.

Various deep learning models have shown excellent results in carrying out image denoising. The greatest advantage of using deep learning for noise reduction is adaptability. As already discussed, there can be different types of noise in an image (based on the source). Deep learning does a great job of addressing various types of image noise without the need for explicit noise categorization.

Research shows that the subjective visual representation of AI-powered noise reduction is superior to traditional methods of denoising. The processing speed of deep learning models is also blazing fast. Once trained, these models provide quick results, thereby, making them ideal candidates for clinical applications.

Autoencoders

The main aim of artificial neural networks is to reproduce a cleaner version of the noisy input data. The networks are trained on noisy medical images and aptly learn the difference between unwanted artifacts and genuine anatomical landmarks. Autoencoders are neural network techniques that are closely related to deep learning and machine learning.

An autoencoder works by compressing the image into a condensed form. These encoders are fed partially corrupted data and trained to remove useless information through dimensionality reduction (a method to represent large data with a lower number of features). It subsequently reconstructs the image while filtering out unwanted noise. And the end result is a clear, noise-free medical image. According to a study, autoencoder algorithms effectively increase the chances of recovering inputs and clearly demonstrating the important features. These noise reduction features enable radiologists and orthodontists to reach a better diagnosis.

U-Network

U-network or U-net is an architectural model that was designed to offer image segmentation. It is a convolutional network used for segmenting medical images. The network is suitable for use in dentistry as dental arch (teeth) segmentation is an important step in orthodontics. Dental image segmentation plays an important role in multiple dental fields. As artificial intelligence is continually growing and evolving, we now have extensions of U-net into denoising.

As U-net is designed for segmentation, its main focus is on precisely highlighting details in a medical image. The denoising effect of U-net precisely localizes the area of concern and filters out noise while ensuring that essential details of the image are accurately retained.

Orthodontists can improve image noise reduction by adopting the latest techniques. U-net and multi-attention mechanisms in AI are novel denoising methods for CT images. The multi-feature attention module learns and extracts valuable information from the input and suppresses invalid data. Another module i.e., the hierarchical attention module uses the deep neural network to extract large amounts of information. An enhanced learning module then stacks the multiple layers to increase the network depth. This results in improved image quality and reduced noise.

Generative Adversarial Networks

You can also use conditional generative adversarial networks (CGANs) to reduce noise in an image. It is the latest deep-learning system for image correction. Research shows that this type of neural network restores corrupted image resolution owing to the image noise. Thus, the results are superior to conventional methods. Investigations are ongoing to reveal CGAN’s role in orthodontic image denoising.

Final Word

The field of diagnostic imaging is plagued with noise. For years, clinicians have been adopting techniques to minimize noise while maintaining the picture quality. The conventional methods of denoising reduce noise but blur the picture. Thus, advanced techniques and practices of noise reduction need to be adopted.

Filtering techniques like anisotropic diffusion filtration and block-matching with 3D transformation show good results with low-noise images. However, for reliable results, we use learning-based methods of artificial intelligence. Deep learning and convolutional neural networks are developed from large datasets. These programs can effectively distinguish noise from true anatomical structures. This helps correct image noise. Artificial neural networks like autoencoders do a great job of eliminating noise. Convolutional-like neural network i.e., U-network is traditionally used to segment teeth but its ability to focus on image details enables orthodontists to use it for denoising images. The latest technique for managing orthodontic image noise is generative adversarial networks.

References

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