Joint low-rank prior and difference of Gaussian filter for magnetic resonance image denoising

Chen, Zhen; Zhou, Zhiheng; Adnan, Saifullah

doi:10.1007/s11517-020-02312-8

Cited by 39 publications

(12 citation statements)

References 48 publications

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“…Some studies have suggested that the image restoration algorithm based on the low-rank theory can achieve better application results by weighting [33]. Chen et al [34] also weighted the RL algorithm with Gaussian noise, and the results showed that the weighted RL algorithm demonstrated a better image processing effect, which supported the results of this study.…”

Section: Discussionsupporting

confidence: 85%

Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer

Luo

Zeng

Chen

2022

Computational and Mathematical Methods in Medicine

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The rapid increase in prostate cancer (PCa) patients is similar to that of benign prostatic hyperplasia (BPH) patients, but the treatments are quite different. In this research, magnetic resonance imaging (MRI) images under the weighted low-rank matrix restoration algorithm (RLRE) were utilized to differentiate PCa from BPH. The diagnostic effects of different sequences of MRI images were evaluated to provide a more effective examination method for the clinical differential diagnosis of PCa and BPH. 150 patients with suspected PCa were taken as the research objects. Pathological examination revealed that 137 patients had PCa and 13 patients had BPH. The pathological results were the gold standard and were compared with the MRI results of different sequences. Therefore, the accuracy of the MRI results was evaluated. The results showed that with the rise of Gaussian noise, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of all three algorithms gradually decreased, but the PSNR and SSIM of the RLRE algorithm were always higher than those of the RL and BM3D algorithms ( P < 0.05 ). The sensitivity (97.08%), specificity (92.31%), accuracy (96.67%), and consistency (0.678) of the dynamic contrast enhancement (DCE) sequence were higher than those of the plain scan (86.13%, 69.23%, 84.67%, and 0.469, respectively). In conclusion, the RLRE algorithm could promote the resolution of MRI images and improve the display effect. DCE could better differentiate PCa from BPH, had great clinical application value, and was worthy of clinical promotion.

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Section: Discussionsupporting

confidence: 85%

Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer

Luo

Zeng

Chen

2022

Computational and Mathematical Methods in Medicine

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“…In the proposed model, Gaussian filter is utilized to remove the unwanted features and noises from the images during feature extraction process. Gaussian filter computes the distribution of the pixel strength in frame [21]. Pixel strength is a combination of probability of intensity and Gaussian function in frame at time, 't'.…”

Section: Pre-processing Stagementioning

confidence: 99%

Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

Oliver¹,

Suresh²,

Mohanarathinam³

et al. 2022

Computers, Materials &Amp; Continua

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Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices. The collected images are then preprocessed using Gaussian filter. Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images. Afterwards, the preprocessed images are sent to prediction phase. In this phase, Deep Dense Convolutional Neural Network (DDCNN) is applied upon the pre-processed images. The proposed classifier is optimally designed with the consideration of Oppositional-based Chimp Optimization Algorithm (OCOA). This algorithm is utilized in the selection of optimal parameters for the proposed classifier. Finally, the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19. The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements. The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm (CNN-FA), Emperor Penguin Optimization (CNN-EPO)

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“…Since the appearance of compressed sensing [15], a huge amount of applications has been proposed [16][17][18][19]. With the booming compressed sensing, the sparse PR has been exploited to obtain robust retrieval without requiring massive measurements.…”

Section: Related Workmentioning

confidence: 99%

Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning

Zhang

Sun

Bai

et al. 2022

IET Signal Processing

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Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unacceptable compute complexity. We derive an algorithm for PR of block sparsity via variational Bayesian learning with expectation maximisation to mitigate this drawback. In the proposed algorithm, the block‐sparse structure is modelled by the hierarchical constructional priors with a novel adaptive coupled pattern, which provides a strong relationship between the neighbour blocks. Simulations indicate that the proposed algorithm outperforms the existing methods in success rate, noise‐robustness, and signal detection rate in large‐scale cases with acceptable computation complexity.

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Joint low-rank prior and difference of Gaussian filter for magnetic resonance image denoising

Cited by 39 publications

References 48 publications

Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer

Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer

Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning

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