Non Linear Tensor Diffusion Based Unsharp Masking for Filtering of COVID-19 CT Images

Kumar, S. N.; Fred, A. Lenin; Miriam, L. R. Jonisha; Gulyás, Balázs; Kumar, Himanshu

doi:10.1007/978-981-15-8534-0_22

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Kumar et al [29] proposed non-linear tensor diffusion-based filtering (NTDF) model for COVID-19 CT images. In the pre-processing stage, the major objective is minimizing noise, removing artifacts and aliasing effects.…”

Section: Related Workmentioning

confidence: 99%

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An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction

Sarvari

Sridevi

2023

Biomedical Signal Processing and Control

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Section: Related Workmentioning

confidence: 99%

“… Fails to order the smooth patch of the pixel image. PSNR-45.80 RMSE-0.007 SSIM-0.83 Kumar et al [29] NTDF CT image Noise reduction It does not require any pre-processing stage to filter the noise from the image. Lack of edge preservation.…”

Section: Related Workmentioning

confidence: 99%

An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction

Sarvari

Sridevi

2023

Biomedical Signal Processing and Control

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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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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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Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images

Rajagopal¹,

Karthick²,

Meenalochini³

et al. 2023

Biomedical Signal Processing and Control

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Non Linear Tensor Diffusion Based Unsharp Masking for Filtering of COVID-19 CT Images

Cited by 4 publications

References 11 publications

An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction

An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction

Denoising of computed tomography using bilateral median based autoencoder network

Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images

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