A region-adaptive non-local denoising algorithm for low-dose computed tomography images

Pengcheng Zhang; Yi Liu; Zhiguo Gui; Yang Chen; Lina Jia; Pengcheng Zhang; Yi Liu; Zhiguo Gui; Yang Chen; Lina Jia

doi:10.3934/mbe.2023133

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 2831-2846. doi: 10.3934/mbe.2023133

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A region-adaptive non-local denoising algorithm for low-dose computed tomography images

1.
State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, China
2.
Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
3.
School of Physics and Electronic Engineering, Shanxi University, Taiyuan, Shanxi 030006, China

Academic Editor: Simon James Fong

Received: 29 September 2022 Revised: 05 November 2022 Accepted: 09 November 2022 Published: 01 December 2022

Low-dose computed tomography (LDCT) can effectively reduce radiation exposure in patients. However, with such dose reductions, large increases in speckled noise and streak artifacts occur, resulting in seriously degraded reconstructed images. The non-local means (NLM) method has shown potential for improving the quality of LDCT images. In the NLM method, similar blocks are obtained using fixed directions over a fixed range. However, the denoising performance of this method is limited. In this paper, a region-adaptive NLM method is proposed for LDCT image denoising. In the proposed method, pixels are classified into different regions according to the edge information of the image. Based on the classification results, the adaptive searching window, block size and filter smoothing parameter could be modified in different regions. Furthermore, the candidate pixels in the searching window could be filtered based on the classification results. In addition, the filter parameter could be adjusted adaptively based on intuitionistic fuzzy divergence (IFD). The experimental results showed that the proposed method performed better in LDCT image denoising than several of the related denoising methods in terms of numerical results and visual quality.
- low-dose computed tomography,
- non-local means,
- intuitionistic fuzzy divergence,
- image denoising,
- edge detection
Citation: Pengcheng Zhang, Yi Liu, Zhiguo Gui, Yang Chen, Lina Jia. A region-adaptive non-local denoising algorithm for low-dose computed tomography images[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2831-2846. doi: 10.3934/mbe.2023133

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Abstract

Low-dose computed tomography (LDCT) can effectively reduce radiation exposure in patients. However, with such dose reductions, large increases in speckled noise and streak artifacts occur, resulting in seriously degraded reconstructed images. The non-local means (NLM) method has shown potential for improving the quality of LDCT images. In the NLM method, similar blocks are obtained using fixed directions over a fixed range. However, the denoising performance of this method is limited. In this paper, a region-adaptive NLM method is proposed for LDCT image denoising. In the proposed method, pixels are classified into different regions according to the edge information of the image. Based on the classification results, the adaptive searching window, block size and filter smoothing parameter could be modified in different regions. Furthermore, the candidate pixels in the searching window could be filtered based on the classification results. In addition, the filter parameter could be adjusted adaptively based on intuitionistic fuzzy divergence (IFD). The experimental results showed that the proposed method performed better in LDCT image denoising than several of the related denoising methods in terms of numerical results and visual quality.

References

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