A 3D MRI denoising algorithm based on Bayesian theory

被引:0
作者
Fabio Baselice
Giampaolo Ferraioli
Vito Pascazio
机构
[1] University of Naples Parthenope,Dipartimento di Ingegneria
[2] University of Naples Parthenope,Dipartimento di Scienze e Tecnologie
来源
BioMedical Engineering OnLine | / 16卷
关键词
3D MRI denoising; Maximum a posteriori; Markov random fields; Statistical signal processing;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 72 条
[1]  
Mohan J(2014)A survey on the magnetic resonance image denoising methods Biomed Signal Process Control 9 56-69
[2]  
Krishnaveni V(2005)A review of image denoising algorithms, with a new one IAM J Multiscale Model Simul SIAM Interdiscip J 4 490-530
[3]  
Guo Y(2012)Review of noise reducing algorithms for brain MRI images Int J Tech Phys Probl Eng 4 54-59
[4]  
Buades A(2007)Sequential anisotropic Wiener filtering applied to 3D MRI data Magn Reson Imaging 25 278-292
[5]  
Coll B(1998)Maximum-likelihood estimation of rician distribution parameters IEEE Trans Med Imaging 17 357-361
[6]  
Morel J-M(2007)Image denoising by sparse 3-d transform-domain collaborative filtering IEEE Trans Image Process 16 2080-2095
[7]  
Balafar MA(1998)fMRI signal restoration using a spatio-temporal markov random field preserving transitions NeuroImage 8 340-349
[8]  
Martin-Fernandez M(2016)A Bayesian approach for relaxation times estimation in MRI Magn Reson Imaging 34 312-325
[9]  
Alberola-Lopez C(2014)Optimal configuration for relaxation times estimation in complex spin echo imaging Sensors 14 2182-3625
[10]  
Ruiz-Alzola J(2010)Relaxation time estimation from complex magnetic resonance images Sensors 10 3611-176