Simulation and Experimental Studies of Optimization of σ-Value for Block Matching and 3D Filtering Algorithm in Magnetic Resonance Images

In this study, we optimized the σ-values of a block matching and 3D filtering (BM3D) algorithm to reduce noise in magnetic resonance images. Brain T2-weighted images (T2WIs) were obtained using the BrainWeb simulation program and Rician noise with intensities of 0.05, 0.10, and 0.15. The BM3D algori...

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Vydané v:Applied sciences Ročník 13; číslo 15; s. 8803
Hlavní autori: Park, Minji, Kang, Seong-Hyeon, Kim, Kyuseok, Lee, Youngjin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.07.2023
Predmet:
Algorithms
brain T2 weighted image
Comparative analysis
Gamma rays
Magnetic fields
magnetic resonance image
Magnetic resonance imaging
Medical imaging
Medical imaging equipment
Methods
Noise control
noise reduction algorithm
Optimization
optimization of block matching and 3D filtering algorithm
quantitative evaluation of image qualities
Radiation
Random variables
Rician noise
Values
Video compression
Wavelet transforms
ISSN:2076-3417, 2076-3417
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Abstract In this study, we optimized the σ-values of a block matching and 3D filtering (BM3D) algorithm to reduce noise in magnetic resonance images. Brain T2-weighted images (T2WIs) were obtained using the BrainWeb simulation program and Rician noise with intensities of 0.05, 0.10, and 0.15. The BM3D algorithm was applied to the optimized BM3D algorithm and compared with conventional noise reduction algorithms using Gaussian, median, and Wiener filters. The clinical feasibility was assessed using real brain T2WIs from the Alzheimer’s Disease Neuroimaging Initiative. Quantitative evaluation was performed using the contrast-to-noise ratio, coefficient of variation, structural similarity index measurement, and root mean square error. The simulation results showed optimal image characteristics and similarity at a σ-value of 0.12, demonstrating superior noise reduction performance. The optimized BM3D algorithm showed the greatest improvement in the clinical study. In conclusion, applying the optimized BM3D algorithm with a σ-value of 0.12 achieved efficient noise reduction.
AbstractList In this study, we optimized the σ-values of a block matching and 3D filtering (BM3D) algorithm to reduce noise in magnetic resonance images. Brain T2-weighted images (T2WIs) were obtained using the BrainWeb simulation program and Rician noise with intensities of 0.05, 0.10, and 0.15. The BM3D algorithm was applied to the optimized BM3D algorithm and compared with conventional noise reduction algorithms using Gaussian, median, and Wiener filters. The clinical feasibility was assessed using real brain T2WIs from the Alzheimer’s Disease Neuroimaging Initiative. Quantitative evaluation was performed using the contrast-to-noise ratio, coefficient of variation, structural similarity index measurement, and root mean square error. The simulation results showed optimal image characteristics and similarity at a σ-value of 0.12, demonstrating superior noise reduction performance. The optimized BM3D algorithm showed the greatest improvement in the clinical study. In conclusion, applying the optimized BM3D algorithm with a σ-value of 0.12 achieved efficient noise reduction.
Audience Academic
Author Kim, Kyuseok
Lee, Youngjin
Park, Minji
Kang, Seong-Hyeon
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CorporateAuthor for the Alzheimer’s Disease Neuroimaging Initiative
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Snippet In this study, we optimized the σ-values of a block matching and 3D filtering (BM3D) algorithm to reduce noise in magnetic resonance images. Brain T2-weighted...
In this study, we optimized the σ -values of a block matching and 3D filtering (BM3D) algorithm to reduce noise in magnetic resonance images. Brain T2-weighted...
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SubjectTerms Algorithms
brain T2 weighted image
Comparative analysis
Gamma rays
Magnetic fields
magnetic resonance image
Magnetic resonance imaging
Medical imaging
Medical imaging equipment
Methods
Noise control
noise reduction algorithm
Optimization
optimization of block matching and 3D filtering algorithm
quantitative evaluation of image qualities
Radiation
Random variables
Rician noise
Values
Video compression
Wavelet transforms
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Title Simulation and Experimental Studies of Optimization of σ-Value for Block Matching and 3D Filtering Algorithm in Magnetic Resonance Images
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