Non-additive noise reduction in medical images using bilateral filtering and modular neural networks

Kalaiyarasi, M.; Janaki, R.; Sampath, A.; Ganage, D. G.; Chincholkar, Yugendra devidas; Budaraju, Srinivas

doi:10.1007/s00500-023-08968-2

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Each value in the table is the summed average value of PSNR and MSSIM of multiple images in the corresponding image group, providing an accurate representation of each group's performance. The average values in Tables 1-3 show that the proposed denoising method upgraded the PSNR, and MSSIM of noisy MRIs significantly, and improved them more significantly than bilateral filtering [42], anisotropic diffusion (AD) [43], nonlocal means filtering with non-subsampled shearlet transform (NST-NLM filtering) [44], Gaussian filtering [45], adaptive blockmatching and 3D (ABM3D) filtering [46], and nonlocal low-rank tensor approximation with logarithmic-sum regularization (NLRTA-LSR), filtering [47] for all noise levels (3%, 5%, 7%, 9%).…”

Section: Denoising Results On Synthetic Mrismentioning

confidence: 99%

“…Table 5 shows the average denoising results obtained from real brain datasets (113 2D-T1w, 184 2D-T2w, 136 2D-PDw, 581 3D-T1w, 578 3D-T2w, 578 3D-PDw). Methods PSNR (dB) MSSIM Bilateral filtering [42] 27.2930 0.7196…”

Section: Evaluation Of Real Clinical Mrismentioning

confidence: 99%

“…Bilateral filtering [42] 27.2930 0.7196 AD [43] 24.6760 0.6456 NST-NLM filtering [44] 31.5442 0.9021 Gaussian filtering [45] 25.5867 0.6612 ABM3D filtering [46] 31.4061 0.8913 NLRTA-LSR [47] 35.1065 0.9486 VST-MCAATE 38.7532 0.9781…”

Section: Psnr (Db) Mssimmentioning

confidence: 99%

“…(a-c) are PSNR value histogram of different methods in T1w, PDw, T2w MRIs, separately; (d-f) are MSSIM value histogram of different methods in T1w, PDw, T2w MRIs, separately. Kalaiyarasi, 2023, used Bilateral filtering[42]; Gupta, 2021, used AD[43]; Sharma, 2021, used NST-NLM filtering[44]; Tyagi, 2018, used Gaussian filtering[45]; Yahya, 2020, used ABM3D filtering[46]; Wang, 2022, used NLRTA-LSR[47].…”

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confidence: 99%

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Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Awudong,

Yakupu,

Yan

et al. 2024

Mathematics

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The inevitable noise generated in the acquisition and transmission process of MRIs seriously affects the reliability and accuracy of medical research and diagnosis. The denoising effect for Rician noise, whose distribution is related to MR image signal, is not good enough. Furthermore, the brain has a complex texture structure and a small density difference between different parts, which leads to higher quality requirements for brain MR images. To upgrade the reliability and accuracy of brain MRIs application and analysis, we designed a new and dedicated denoising algorithm (named VST–MCAATE), based on their inherent characteristics. Comparative experiments were performed on the same simulated and real brain MR datasets. The peak signal-to-noise ratio (PSNR), and mean structural similarity index measure (MSSIM) were used as objective image quality evaluation. The one-way ANOVA was used to compare the effects of denoising between different approaches. p < 0.01 was considered statistically significant. The experimental results show that the PSNR and MSSIM values of VST–MCAATE are significantly higher than state-of-the-art methods (p < 0.01), and also that residual images have no anatomical structure. The proposed denoising method has advantages in improving the quality of brain MRIs, while effectively removing the noise with a wide range of unknown noise levels without damaging texture details, and has potential clinical promise.

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Section: Denoising Results On Synthetic Mrismentioning

confidence: 99%

Section: Evaluation Of Real Clinical Mrismentioning

confidence: 99%

Section: Psnr (Db) Mssimmentioning

confidence: 99%

mentioning

confidence: 99%

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Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Awudong,

Yakupu,

Yan

et al. 2024

Mathematics

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An intelligent generative adversarial network multistage lung cancer detection and subtypes classification

Aharonu,

Ramasamy

2024

Int. J. Mach. Learn. & Cyber.

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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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Non-additive noise reduction in medical images using bilateral filtering and modular neural networks

Cited by 3 publications

References 17 publications

Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

An intelligent generative adversarial network multistage lung cancer detection and subtypes classification

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

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