计算机技术与发展2024，Vol.34Issue(9)：70-76,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0171
基于BM3D的脑MRI图像噪点剔除算法
Complex Domain Noise Removal Algorithm for a Single Brain MRI Image Based on BM3D
摘要
Abstract
Magnetic resonance imaging(MRI)has become a common imaging examination method,and the denoising algorithm of MRI affects the imaging effect.Deep learning based MRI denoising algorithms require a certain amount of data,and the vast majority of non deep learning based MRI denoising algorithms convert MRI data into real numbers for denoising.Algorithms for complex data types in complex MRI data also have distortion issues.Therefore,a noise removal algorithm is proposed based on the raw data of a single MRI brain image to better remove image noise.Starting from the raw data of MRI,the proposed algorithm utilizes the distribution properties of MRI noise and the characteristics of MRI brain images to determine the points with obvious noise in the MRI image,and thus eliminates specific Rician distribution noise in the MRI.And the proposed algorithm was combined with the commonly used Non-Local Means de-noising(NLM)and Block-Matching and3D filtering(BM3D)in MRI denoising,and the denoising effect was compared and evaluated with NLM and BM3D denoising algorithms that did not use this algorithm to remove noise.The comparison results show that in various situations with different noise densities,the proposed algorithm can always optimize the image denoising algorithm combined with it,and improve Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity(SSIM)by 1%to 9%under different noise conditions.Finally,it was combined with BM3D and compared with other MRI denoising algorithms such as DnCNN,Weighted Nuclear Norm Mini-mization(WNNM),BM3D,and NLM.When there is more Rician noise,the proposed algorithm performs better on PSNR.关键词
脑磁共振成像/噪声去除/莱斯分布/非局部平均算法/三维块匹配算法Key words
brain MRI imageing/noise removal/Rician distribution/Non-Local Means denoising(NLM)/Block-Matching and 3D filtering(BM3D)分类
信息技术与安全科学引用本文复制引用
徐梦笔,何刚..基于BM3D的脑MRI图像噪点剔除算法[J].计算机技术与发展,2024,34(9):70-76,7.基金项目
四川省科技项目(2020YFS0454,2020YFS0318) （2020YFS0454,2020YFS0318）
国家卫生健康委员会核技术医学转化重点实验室开放课题资助(2021HYX031) （2021HYX031）
