A review on medical image denoising algorithms

Sagheer, Sameera V. Mohd; George, Sudhish N.

doi:10.1016/j.bspc.2020.102036

Cited by 133 publications

(67 citation statements)

References 109 publications

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“…The edge preservation index (EPI) indicates the amount of edges that are preserved in the test image [ 55 , 56 ]. Edges are of great importance in medical imaging as they contain significant information such as tumor or lesion contour identification [ 55 , 56 ]. The EPI between reference image A and test image B is determined as

…”

Section: Methodsmentioning

confidence: 99%

“…where ∆A and ∆B are obtained from filtering A and B using a high-pass filter [ 55 , 56 ], such as a simple 3 × 3 Laplacian operator-based spatial domain filter H given as

…”

Section: Methodsmentioning

confidence: 99%

“…μ ∆A and μ ∆B represent the mean of ∆A and ∆ B, respectively. The EPI values range between 0 and 1; the higher the value the better is the image quality [ 55 , 56 ].…”

Section: Methodsmentioning

confidence: 99%

“…Sharpness index (SI) is a no-reference image quality metric based on the image Fourier phase spectrum, which contains important information such as image geometry and contour details [ 55 , 57 ]. The sharpness index (SI) of a test image B is defined as

…”

Section: Methodsmentioning

confidence: 99%

“…where TV( B ) refers to the total variation of image B , μ = E (TV( B )), σ 2 = Var(TV( B )), and ∅ is the tail of the Gauss distribution [ 55 , 57 ]. A higher SI value corresponds to better image quality.…”

Section: Methodsmentioning

confidence: 99%

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Region-Based Segmentation and Wiener Pilot-Based Novel Amoeba Denoising Scheme for CT Imaging

et al. 2020

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Computed tomography (CT) is one of the most common and beneficial medical imaging schemes, but the associated high radiation dose injurious to the patient is always a concern. Therefore, postprocessing-based enhancement of a CT reconstructed image acquired using a reduced dose is an active research area. Amoeba- (or spatially variant kernel-) based filtering is a strong candidate scheme for postprocessing of the CT image, which adapts its shape according to the image contents. In the reported research work, the amoeba filtering is customized for postprocessing of CT images acquired at a reduced X-ray dose. The proposed scheme modifies both the pilot image formation and amoeba shaping mechanism of the conventional amoeba implementation. The proposed scheme uses a Wiener filter-based pilot image, while region-based segmentation is used for amoeba shaping instead of the conventional amoeba distance-based approach. The merits of the proposed scheme include being more suitable for CT images because of the similar region-based and symmetric nature of the human body anatomy, image smoothing without compromising on the edge details, and being adaptive in nature and more robust to noise. The performance of the proposed amoeba scheme is compared to the traditional amoeba kernel in the image denoising application for CT images using filtered back projection (FBP) on sparse-view projections. The scheme is supported by computer simulations using fan-beam projections of clinically reconstructed and simulated head CT phantoms. The scheme is tested using multiple image quality matrices, in the presence of additive projection noise. The scheme implementation significantly improves the image quality visually and statistically, providing better contrast and image smoothing without compromising on edge details. Promising results indicate the efficacy of the proposed scheme.

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…”

Section: Methodsmentioning

confidence: 99%

“…where ∆A and ∆B are obtained from filtering A and B using a high-pass filter [ 55 , 56 ], such as a simple 3 × 3 Laplacian operator-based spatial domain filter H given as

…”

Section: Methodsmentioning

confidence: 99%

“…μ ∆A and μ ∆B represent the mean of ∆A and ∆ B, respectively. The EPI values range between 0 and 1; the higher the value the better is the image quality [ 55 , 56 ].…”

Section: Methodsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Region-Based Segmentation and Wiener Pilot-Based Novel Amoeba Denoising Scheme for CT Imaging

et al. 2020

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Improving reproducibility and performance of radiomics in low‐dose CT using cycle GANs

Chen

Wee

Dekker

et al. 2022

J Applied Clin Med Phys

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Background As a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low‐dose CT scans are still poor, mostly due to noise. Deep learning generative models can be used to denoise these images and in turn improve radiomics’ reproducibility and performance. However, most generative models are trained on paired data, which can be difficult or impossible to collect. Purpose In this article, we investigate the possibility of denoising low‐dose CTs using cycle generative adversarial networks (GANs) to improve radiomics reproducibility and performance based on unpaired datasets. Methods and materials Two cycle GANs were trained: (1) from paired data, by simulating low‐dose CTs (i.e., introducing noise) from high‐dose CTs and (2) from unpaired real low dose CTs. To accelerate convergence, during GAN training, a slice‐paired training strategy was introduced. The trained GANs were applied to three scenarios: (1) improving radiomics reproducibility in simulated low‐dose CT images and (2) same‐day repeat low dose CTs (RIDER dataset), and (3) improving radiomics performance in survival prediction. Cycle GAN results were compared with a conditional GAN (CGAN) and an encoder–decoder network (EDN) trained on simulated paired data. Results The cycle GAN trained on simulated data improved concordance correlation coefficients (CCC) of radiomic features from 0.87 (95%CI, [0.833,0.901]) to 0.93 (95%CI, [0.916,0.949]) on simulated noise CT and from 0.89 (95%CI, [0.881,0.914]) to 0.92 (95%CI, [0.908,0.937]) on the RIDER dataset, as well improving the area under the receiver operating characteristic curve (AUC) of survival prediction from 0.52 (95%CI, [0.511,0.538]) to 0.59 (95%CI, [0.578,0.602]). The cycle GAN trained on real data increased the CCCs of features in RIDER to 0.95 (95%CI, [0.933,0.961]) and the AUC of survival prediction to 0.58 (95%CI, [0.576,0.596]). Conclusion The results show that cycle GANs trained on both simulated and real data can improve radiomics’ reproducibility and performance in low‐dose CT and achieve similar results compared to CGANs and EDNs.

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Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet

Benhassine¹,

Boukaache

Boudjehem

2021

Int J Imaging Syst Tech

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Medical images have become omnipresent in diagnosis and therapy. However, they can be affected by various types of noise that reduce image quality and make the final diagnostic decision difficult. The main objective of this research is to effectively remove the noise while preserving the important image characteristics. This paper proposes a novel approach for image denoising based on discrete wavelet transform (DWT) with the selection of the best decomposition level and mother wavelet. Then, the thresholding function is carried out in the detail coefficients. Optimal thresholding is done using new optimization techniques such as the crow search algorithm and social spider optimization techniques. Finally, the inverse of DWT is applied to reconstruct the denoised image. The proposed method is evaluated using peak signal to noise ratio, mean square error, and the structural similarity index measure. The experimental results show the efficiency of the optimization-based denoising method over standard methods. Interesting results are obtained with all kinds of noise, and improvements about 30 dB can be reached with the Rician noise.

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A review on medical image denoising algorithms

Cited by 133 publications

References 109 publications

Region-Based Segmentation and Wiener Pilot-Based Novel Amoeba Denoising Scheme for CT Imaging

Region-Based Segmentation and Wiener Pilot-Based Novel Amoeba Denoising Scheme for CT Imaging

Improving reproducibility and performance of radiomics in low‐dose CT using cycle GANs

Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet

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