estSmooth.RdEstimates smoothness of a single image or image matrix
estSmooth(x, mask, rdf, scaleResid = TRUE, sample = NULL, verbose = TRUE)
| x | may be an image of class "antsImage" or an image matrix |
|---|---|
| mask | input mask, must match matrix |
| rdf | residual degrees of freedom |
| scaleResid | logical. if TRUE residuals are scaled |
| sample | number of images to use for estimating smoothing (default uses all images) |
| verbose | enables verbose output |
Outputs the estimated FWHM and RPV image
The partial derivatives of an image in x, y, and z directions are used to create a covariance matrix which in turn is used to calculate the full-widths at half maxima (FWHM). The FWHM is equivalent to the estimated image smoothness.
The resels per voxel image (RPVImg) represents the estimated resel at
each individual voxel throughout the search region. This may be used in
place of volumetric measurements (or sum voxel measurements) when estimating
the p-value of a cluster using rftPval. The intent behind using the
RPV image to estimate cluster level statistics is to offset the natural
probability of obtaining significant clusters solely by chance in very
smooth regions at low thresholds.
It is possible to use a single statistical field image to estimate the FWHM.
However, it's recommended that FWHM estimates are obtained from the scaled
residuals of statistical models (Stefan J.K et al., 1999). Therefore, this
function is optimized to estimate the pooled smoothness of the residual
images from a fitted model. By default residuals are scaled
(scaleResid = TRUE).
A scaling factor is used to correct for differences when using the
sample option. Scaling isn't effective when the number of images is
very low and typically results in an overestimation of the the FWHM. If only
one image or numeric vector is entered then the scaling factor is not used.
If a numeric vector is entered the imageMake function is used to
prepare it for smoothness estimation (see Worsley et al., 1999).
Any NA values in object will be set to zero.
Hayasaka (2004) Nonstationary cluster-size inference with random field and permutation methods.
Worsley K.J. (1992) A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain.
Worsley K.J. (1996) A Unified Statistical Approach for Determining Significant Signals in Images of Cerebral Activation.
Worsley K.J. (1999) Detecting Changes in Nonisotropic Images
Stefan J.K. (1999) Robust Smoothness Estimation in Statistical Parametric Maps Using Standardized Residual from the General Linear Model
resels
# estimate individual image mnit1 <- antsImageRead(getANTsRData('r16')) mask <- getMask( mnit1 ) fwhm1 <- estSmooth( mnit1, mask )#> Warning: coercing argument of type 'double' to logical#> | | | 0% | |======================================================================| 100%