Low-dose CT image denoising using sparse 3d transformation with probabilistic non-local means for clinical applications

ABSTRACT

The use of low-dose computed tomography (LDCT) in medical practice can effectively reduce the radiation risk of patients, but it may increase noise and artefacts, which can compromise diagnostic information. In this paper, we propose a LDCT denoising method based on sparse 3d transformation with probabilistic non-local means (PNLM). The hard-thresholding module in the sparse 3d transformation framework is used to attenuate noise in the transform coefficients. In addition, the PNLM overcomes the incompetence of non-local means weights problems where its weights better reflect the patch similarities, thereupon it connects the denoising process and the noise type and thus able to denoise complex noise present in LDCT images. Besides, a significant denoising improvement is obtained by using the collaborative wiener filtering. Experiments on NIH-AAPM Mayo Clinic LDCT dataset show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

KEYWORDS:

• Computed tomography (CT)
• low-dose computed tomography (LDCT)
• normal-dose computed tomography (NDCT)
• image denoising
• hard-thresholding
• wiener filtering

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive suggestions and comments which significantly helps to improves the quality of this paper.

Data availability statement

The data used in this paper can be publicly accessed from the official website (https://www.aapm.org/grandchallenge/lowdosect/) of the AAPM Low Dose CT Challenge.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Dawa Chyophel Lepcha

Dawa Chyophel Lepcha received his bachelor's degree in Electronics and Telecommunication Engineering from University of Mumbai, India and his master's degree in Electronics and Communication Engineering from Chandigarh University, Punjab, India. He received his Master of Science (MS) in Electronics and Information Technology from University of South Wales, Wales, United Kingdom. He is currently pursuing his PhD in Electronics and Communication Engineering at Chandigarh University, Punjab, India. He is presently working in the field of medical image processing in order to improve low-quality medical images for clinical applications. His research interests include image processing, signal processing, machine learning and computer vision.

Bhawna Goyal

Bhawna Goyal is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering at Chandigarh University, India. She received her bachelor's degree in Electronics and Communication Engineering from Guru Nanak Dev University, Amritsar, India and master's degree in Electronics and Communication Engineering from PEC University of Technology, Chandigarh, India. She obtained her PhD in Electronics and Communication Engineering from Punjab University, India. She has published numerous papers in highly reputed journals. She has performed reviewer works for pioneering journals like IEEE Access, Measurement and Journal of computer science. Her research areas include biomedical signal and image processing, and computer vision.

Ayush Dogra

Ayush Dogra is currently working as CSIR-NPD fellow at CSIR- CSIO Lab at Chandigarh, India. He received his bachelor's degree in Electronics and Communication Engineering from Guru Nanak Dev University, Amritsar, India and master's degree from Punjabi University, Patiala, India. He has also received his master's degree in Business Management (MBA) from IGNOU, Delhi. He obtained his PhD in Electronics and Communication Engineering from Punjab University, India. His doctoral research focuses on devising a novel and innovative, market-oriented mechanism for medical image processing. He has published numerous papers in highly cited journals and conferences. He is currently doing editorial/reviewing works for various highly reputed SCI/SCIE and Scopus indexed journals.