Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm

L, Shabana Sulthana S; Sucharitha, M.

doi:10.1007/s10278-021-00517-3

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…The suggested method estimates the anatomical structures by resolving a constrained optimization problem that combines wavelet-domain sparsity and total variation (wavelet-TV) regularization in order to preserve the edges of retinal layers and lessen artifacts brought on by pure wavelet thresholding. To accomplish the goal function outlined in [ 17 , 18 ], we use a brand-new hybrid approach called the Randomized FireFly (FF) update in Lion Algorithm (RFU-LA), which includes components of both the Lion Algorithm (LA) and the FireFly Algorithm (FF). By taking the mean of images that have been routinely median-filtered using different kernel sizes, the hybrid median-mean filter (HM2F) proposed in [ 19 ] is a one-shot image processing method for decreasing speckle noise.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Amarnath,

Manoharan,

Natarajan

et al. 2023

Diagnostics

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Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.

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Section: Related Workmentioning

confidence: 99%

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Amarnath,

Manoharan,

Natarajan

et al. 2023

Diagnostics

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Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review

van der Pol,

van Karnenbeek,

Wijkhuizen

et al. 2024

Applied Sciences

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The popularity of handheld devices for point-of-care ultrasound (POCUS) has increased in recent years due to their portability and cost-effectiveness. However, POCUS has the drawback of lower imaging quality compared to conventional ultrasound because of hardware limitations. Improving the quality of POCUS through post-image processing would therefore be beneficial, with deep learning approaches showing promise in this regard. This review investigates the state-of-the-art progress of image enhancement using deep learning suitable for POCUS applications. A systematic search was conducted from January 2024 to February 2024 on PubMed and Scopus. From the 457 articles that were found, the full text was retrieved for 69 articles. From this selection, 15 articles were identified addressing multiple quality enhancement aspects. A disparity in the baseline performance of the low-quality input images was seen across these studies, ranging between 8.65 and 29.24 dB for the Peak Signal-to-Noise Ratio (PSNR) and between 0.03 an 0.71 for the Structural Similarity Index Measure (SSIM). In six studies, where both the PSNR and the SSIM metrics were reported for the baseline and the generated images, mean differences of 6.60 (SD ± 2.99) and 0.28 (SD ± 0.15) were observed for the PSNR and SSIM, respectively. The reported performance outcomes demonstrate the potential of deep learning-based image enhancement for POCUS. However, variability in the extent of the performance gain across datasets and articles was notable, and the heterogeneity across articles makes quantifying the exact improvements challenging.

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Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm

Cited by 2 publications

References 38 publications

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review

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