Automatic estimation of the noise variance from the histogram of a magnetic resonance image.
Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks. In the literature, various methods for noise variance estimation from MR images are available, most of which however require user interaction and/or multiple (perfectly aligned) images. In this paper, we focus on automatic histogram-based noise variance estimation techniques. Previously described methods are reviewed and a new method based on the maximum likelihood (ML) principle is presented. Using Monte Carlo simulation experiments as well as experimental MR data sets, the noise variance estimation methods are compared in terms of the root mean squared error (RMSE). The results show that the newly proposed method is superior in terms of the RMSE.
Sijbers
Jan
J
Vision Lab, Department of Physics, University of Antwerp Universiteitsplein 1, B-2610 Wilrijk, Belgium. jan.sijbers@ua.ac.be
Poot
Dirk
D
den Dekker
Arnold J
AJ
Pintjens
Wouter
W
D016477:Artifacts; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity