awsLocalSigma {aws} | R Documentation |

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)

`y` |
3D array of image intensities. |

`steps` |
number of steps in adapive weights smoothing, used to reveal the unerlying mean structure. |

`mask` |
restrict computations to voxel in mask, if |

`ncoils` |
effective number of coils, or equivalently number of effective degrees of freedom of non-central chi distribution divided by 2. |

`vext` |
voxel extentions or relative voxel extensions |

`lambda` |
scale parameter in adaptive weights smoothing |

`minni` |
minimal bandwidth for calculating local variance estimates |

`hsig` |
bandwwidth for median filter |

`sigma` |
optional initial global variance estimate |

`family` |
type of distribution, either noncentral Chi ("NCchi") or Gaussian ("Gauss") |

`verbose` |
if |

`trace` |
if |

`u` |
if |

`hmax` |
maximal bandwidth |

`hpre` |
minimal bandwidth |

`h0` |
bandwidth vector characterizing to spatial correlation as correlation induced by convolution with a Gaussian kernel |

`ladjust` |
correction factor for lambda |

`wghts` |
relative voxel extensions |

`varprop` |
defines a lower bound for the estimated variance as |

`A0` |
select voxel with |

`A1` |
select voxel with |

`level` |
threshold for mask definition |

`h` |
bandwidth for local variance estimates. |

`hinit` |
minimal bandwidth for local variance estimates with |

`hadj` |
bandwidth for mode estimation |

`q` |
Quantile for interquantile estimate of standard deviation |

`sequence` |
logical, return sequence of estimated variances for iterative methods. |

`method` |
determines variance estimation method |

`magnitude` |
magnitude of complex 3D image |

`phase` |
phase of complex 3D image |

`kstar` |
number of steps in adapive weights smoothing, used to reveal the unerlying mean structure. |

`kmin` |
iteration to start adaptation |

all functions return lists with variance estimates in component `sigma`

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.

[Package *aws* version 2.5-1 Index]