Noise reduction in dental CT images based on generative adversarial network

Heng Shao; Haiyan Gu; Peixi Liao; Hu Chen; Yi Zhang

doi:10.1117/12.2623120

16 February 2022 Noise reduction in dental CT images based on generative adversarial network

Heng Shao, Haiyan Gu, Peixi Liao, Hu Chen, Yi Zhang

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208326 (2022) https://doi.org/10.1117/12.2623120
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

CT image technology has been widely used in recent research on dental imaging. High-quality CT images can be accessed by adopting a high-dose radiation dose that unfortunately exerts a detrimental influence on the human body. Reducing the radiation dose is a solution to eliminate the negative effect, while it will put noise into the CT images. In this paper, a dental CT image denoising method is proposed based on the generative adversarial network. A total of 6144 pairs in which each contained a noise dental CT image and a corresponding original dental CT image were trained through the model of the generative adversarial network. The test was carried out on the original untrained image. Comparing with the image without denoising, the test results indicate a significant improvement on the results. For example, the PSNR is increased by 21.52% and the SSIM is increased by 52.95%. In the comparison with the professional artificial vision performance, the result has also been significantly improved, which proves the effectiveness of the method in this paper. Moreover, the method proposed in this paper merely needs little training data. It is foreseeable that with the increasing of the training data, this method will have a better performance in the aspect of noise reduction in dental CT images.

Citation Download Citation

Heng Shao, Haiyan Gu, Peixi Liao, Hu Chen, and Yi Zhang "Noise reduction in dental CT images based on generative adversarial network", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208326 (16 February 2022); https://doi.org/10.1117/12.2623120

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