Functions for 3D variance estimation. awsLocalSigma implements the
local adaptive variance estimation procedure introduced in Tabelow, Voss and Polzehl (2015).
awslinsd uses a parametric model for varianc/mesn dependence. Functions
AFLocalSigma and estGlobalSigma implement various proposals for local
and global variance estimates from Aja-Fernandez (2009, 2013) and a global variant of the
approach from Tabelow, Voss and Polzehl (2015).
awsLocalSigma(y, steps, mask, ncoils, vext = c(1, 1), lambda = 5,
minni = 2, hsig = 5, sigma = NULL, family = c("NCchi", "Gauss"),
verbose = FALSE, trace = FALSE, u = NULL)
awslinsd(y, hmax = NULL, hpre = NULL, h0 = NULL, mask = NULL,
ladjust = 1, wghts = NULL, varprop = 0.1, A0, A1)
AFLocalSigma(y, ncoils, level = NULL, mask = NULL, h = 2, hadj = 1,
vext = c(1, 1))
estGlobalSigma(y, mask = NULL, ncoils = 1, steps = 16, vext = c(1, 1),
lambda = 20, hinit = 2, hadj = 1, q = 0.25, level = NULL,
sequence = FALSE, method = c("awsVar", "awsMAD", "AFmodevn",
"AFmodem1chi", "AFbkm2chi", "AFbkm1chi"))
estimateSigmaCompl(magnitude, phase, mask, kstar = 20, kmin = 8, hsig = 5,
lambda = 12, verbose = TRUE)all functions return lists with variance estimates in component sigma
3D array of image intensities.
number of steps in adapive weights smoothing, used to reveal the unerlying mean structure.
restrict computations to voxel in mask, if is.null(mask) all voxel are used.
In function estGlobalSigma mask should refer to background for method %in% c("modem1chi","bkm2chi","bkm1chi") and to voxel within the head for
method=="modevn".
effective number of coils, or equivalently number of effective degrees of freedom of non-central chi distribution divided by 2.
voxel extentions or relative voxel extensions
scale parameter in adaptive weights smoothing
minimal bandwidth for calculating local variance estimates
bandwwidth for median filter
optional initial global variance estimate
type of distribution, either noncentral Chi ("NCchi") or Gaussian ("Gauss")
if verbose==TRUE density plots
and quantiles of local estimates of sigma are provided.
if trace==TRUE intermediate results for each step are
returned in component tergs for all voxel in mask.
if verbose==TRUE an array of noncentrality paramters for
comparisons. Internal use for tests only
maximal bandwidth
minimal bandwidth
bandwidth vector characterizing to spatial correlation as correlation induced by convolution with a Gaussian kernel
correction factor for lambda
relative voxel extensions
defines a lower bound for the estimated variance as varprop*mean(sigma2hat
select voxel with A0 < theta < A1 to estimate parameters of the variance model
select voxel with A0 < theta < A1 to estimate parameters of the variance model
threshold for mask definition
bandwidth for local variance estimates.
minimal bandwidth for local variance estimates with method="awsxxx".
bandwidth for mode estimation
Quantile for interquantile estimate of standard deviation
logical, return sequence of estimated variances for iterative methods.
determines variance estimation method
magnitude of complex 3D image
phase of complex 3D image
number of steps in adapive weights smoothing, used to reveal the unerlying mean structure.
iteration to start adaptation
J\"org Polzehl polzehl@wias-berlin.de
K. Tabelow, H.U. Voss, J. Polzehl, Local estimation of the noise level in MRI using structural adaptation, Medical Image Analysis, 20 (2015), 76--86. DOI:10.1016/j.media.2014.10.008.
S. Aja-Fernandez, V. Brion, A. Tristan-Vega, Effective noise estimation and filtering from correlated multiple-coil MR data. Magn Reson Imaging, 31 (2013), 272-285. DOI:10.1016/j.mri.2012.07.006
S. Aja-Fernandez, A. Tristan-Vega, C. Alberola-Lopez, Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models. Magn Reson Imaging, 27 (2009), 1397-1409. DOI:10.1016/j.mri.2009.05.025.