Edge-Preserving Median Filter and Weighted Coding with Sparse Nonlocal Regularization for Low-Dose CT Image Denoising Algorithm

The impulse noise in CT image was removed based on edge-preserving median filter algorithm. The sparse nonlocal regularization algorithm weighted coding was used to remove the impulse noise and Gaussian noise in the mixed noise, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated to evaluate the quality of the denoised CT image. It was found that in nine different proportions of Gaussian noise and salt-and-pepper noise in Shepp-Logan image and CT image processing, the PSNR and SSIM values of the proposed denoising algorithm based on edge-preserving median filter (EP median filter) and weighted encoding with sparse nonlocal regularization (WESNR) were significantly higher than those of using EP median filter and WESNR alone. It was shown that the weighted coding algorithm based on edge-preserving median filtering and sparse nonlocal regularization had potential application value in low-dose CT image denoising.

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Adaptively Tuned Iterative Low Dose CT Image Denoising

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/638568 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

SayedMasoud Hashemi ◽

Narinder S. Paul ◽

Soosan Beheshti ◽

Richard S. C. Cobbold

Keyword(s):

Image Denoising ◽

Low Dose ◽

Ad Hoc ◽

Confidence Region ◽

Similarity Index ◽

Structural Similarity ◽

Block Matching ◽

Ct Image ◽

Denoising Method ◽

Low Dose Ct

Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction.

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Low-Dose CT Image Denoising and Pulmonary Nodule Identification

Proceedings of the 2019 2nd International Conference on Sensors, Signal and Image Processing - SSIP 2019 ◽

10.1145/3365245.3365252 ◽

2019 ◽

Author(s):

Zheng Chen ◽

Zhengping Yong

Keyword(s):

Image Denoising ◽

Pulmonary Nodule ◽

Low Dose ◽

Ct Image ◽

Low Dose Ct

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Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

Scientific Programming ◽

10.1155/2021/7008406 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Quan Yuan ◽

Zhenyun Peng ◽

Zhencheng Chen ◽

Yanke Guo ◽

Bin Yang ◽

...

Keyword(s):

Image Denoising ◽

Gaussian Noise ◽

Medical Image ◽

Impulse Noise ◽

Medical Images ◽

Noise Removal ◽

White Gaussian Noise ◽

Low Rank ◽

Rank Constraint ◽

Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

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