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Denoising techniques for cephalometric x-ray images: A comprehensive review

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Abstract

Noising in X-ray imaging has been one of the biggest challenges that leads to insufficient and improper diagnosis. Despite the fact that X-rays are one of the most widespread and acceptable imaging techniques among the medical and scientific fraternity, still Gaussian and Poisson noise lead to a lot of image deterioration. Over the past few decades, several denoising techniques have been explored using traditional, hybrid and deep learning techniques which have been reported in this paper. Poisson noise was best removed by the application of bilateral filter with a maximum Peak Signal to Noise Ratio (PSNR) of 36.22 and for the removal of Gaussian noise, median filter proved to be unparalleled with a PSNR of 32.92 for the variance of 0.01, 31.4 for the variance of 0.04, 31.03 for the variance of 0.07, and 30.58 for the variance of 0.1 amongst the conventional filters. The Noise2Noise model employing the deep learning approach has given the best PSNR value of 34.38 amongst all the other alternatives for the images with gaussian noise. This paper serves as a comprehensive review for beginners working in this domain, that would aid them to select the best filter for the image pre-processing and noise removal.

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Acknowledgements

The authors are grateful to the Ministry of Human Resource Development (MHRD), Govt. of India for funding this project under Design Innovation Centre (DIC) sub-theme Medical Devices & Restorative Technologies.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Mamta Juneja, Janmejai Singh Minhas, Naveen Singla, Ravinder Kaur & Prashant Jindal

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Juneja, M., Minhas, J.S., Singla, N. et al. Denoising techniques for cephalometric x-ray images: A comprehensive review. Multimed Tools Appl 83, 49953–49991 (2024). https://doi.org/10.1007/s11042-023-17495-z

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