estSmooth: Estimate image smoothness

In Tokazama/iClass: More intuitive syntax for manipulating imaging data

Description

Estimates smoothness of a single image or image matrix

Usage

estSmooth(x, mask, rdf, scaleResid = TRUE, sample = NULL, verbose = TRUE)

Arguments

x	May be an image of class "antsImage" or an image matrix.
mask	Input mask, must match matrix.
rdf	Residual degrees of freedom.
scaleResid	If TRUE residuals are scaled.
sample	Number of images to use for estimating smoothing (default uses all images).

Details

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 (rpvImage) 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.

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.

Value

Outputs the estimated FWHM and resel per voxel image.

Author(s)

Zachary P. Christensen

References

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

See Also

resels

Examples

# estimate individual image
mnit1 <- antsImageRead(getANTsRData('mni'))
mask <- getMask(mnit1)
fwhm1 <- estSmooth(mnit1, mask)

Tokazama/iClass documentation built on May 9, 2019, 4:51 p.m.