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 1 publication

(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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