A wavelet-based method for MRI liver image denoising

Denoising of Computed tomography (CT) images is a critical aspect of image processing that is expected to improve the performance of Computer‐aided diagnosis (CAD) systems. However, the use of complex imaging modalities such as CT imaging to ascertain pancreatic cancer is vulnerable to gaussian and poisson noises, making image denoising an imperative step for the accurate performance of CAD systems. This paper presents a Bilateral median based autoencoder network (BMAuto‐Net) constructed with intermediate batch normalization layers and dropout factors to eliminate gaussian noise from the CT images. The skip connections adjoining the network, prevent performance degradation that generally occurs in most autoencoder architectures. Based on the presented study, BMAuto‐Net is reckoned to outperform other traditional filters and autoencoders. The performance measurement of the proposed architecture is performed using the peak signal‐to‐noise ratio (PSNR), mean squared error (MSE), and structured similarity index (SSIM) metric values. The Cancer imaging archive (TCIA) dataset consisting of 19 000 CT images is used to validate the performance of the architecture with average PSNR values of 30.01, 30.53, and 30.52, MSE values of 98.23, 98.87, and 98.94, and SSIM of values of 0.67, 0.60, and 0.57 for noise factors (NFs) of 0.1, 0.3, and 0.5 respectively.

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“…The performance metrics used to evaluate the performance of the algorithm were SNR, PSNR, and MSE. 32…”

Section: Wavelet Filtermentioning

confidence: 99%

“…Ali MN in 2019, proposed the wavelet transform method to denoise the CT images by selecting wavelet families and thresholding methods with appropriate decomposition levels. The performance metrics used to evaluate the performance of the algorithm were SNR, PSNR, and MSE 32 …”

Section: State‐of‐the‐art Filtersmentioning

confidence: 99%

Denoising of computed tomography using bilateral median based autoencoder network

Juneja

Joshi

Singla

et al. 2021

Int J Imaging Syst Tech

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“…If pixels are non-smooth then the size of the window will be big and the denoising region will be a blur and if the pixels are smooth then the window will be small and the resultant image will be different from the original image. The wavelet method was used for denoising the MRI in [14] with an estimation of PSNR and MSE. This method was developed by Md.…”

Section: Related Workmentioning

confidence: 99%

Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things

Singla

Verma

et al. 2020

IEEE Access

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Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment. These images suffer from inadequate contrast and noise that occurs during image acquisition. Thus, denoising and contrast enhancement is crucial in increasing the visual quality of the images for obtaining quantitative measures. In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities. To evaluate and validate, the convolution neural network utilizes patch creation and dictionary methods for obtaining information. The proposed framework is predominant to other current approaches by employing image assessment quantitative measures like peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The study also optimizes the computational time to achieve increased efficiency and better visual quality of the image. Furthermore, the widespread use of the Internet of Healthcare Things (IoHT) helps to provide security with vault and challenge schemes between IoT devices and servers.

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“…In the paper, a detailed survey of different types of noises, its parameters and different methods applied for image denoising is discussed. This issue of denoising can be resolved by the wavelet-based method [15]. The liver MRI is decomposed into a set of functions then the corresponding wavelet signals are used for sampling.…”

Section: Literature Reviewmentioning

confidence: 99%

Critical Findings on Restoration of Magnetic Resonance Images

More*,

Singla

2020

IJITEE

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The explosion of numerous medical images lead to the development of many different techniques to provide an accurate result. Although the signal to noise ratio (SNR), resolution and speed of magnetic resonance imaging technology have increased, still magnetic resonance images are affected by noise, contrast, and artifacts. To provide the image content or features relevant to diagnosis, contrast enhancement and reduction of noise with preservation of actual content should be carried out. The purpose of this paper is to present a critical review of different types of noises with an overview of diverse techniques for denoising and contrast enhancement for magnetic resonance images and discuss the advantages and limitations of these techniques with broad ideology

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A wavelet-based method for MRI liver image denoising

Cited by 12 publications

References 11 publications

Denoising of computed tomography using bilateral median based autoencoder network

Denoising of computed tomography using bilateral median based autoencoder network

Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things

Critical Findings on Restoration of Magnetic Resonance Images

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