Estimate image smoothness

Estimates smoothness of a single image or image matrix

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

logical. if TRUE residuals are scaled

sample

number of images to use for estimating smoothing (default uses all images)

verbose

enables verbose output

Value

Outputs the estimated FWHM and RPV image

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 (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.

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

Author

Zachary P. Christensen

Examples

# estimate individual image
mnit1 <- antsImageRead(getANTsRData("r16"))
mask <- getMask(mnit1)
fwhm1 <- estSmooth(mnit1, mask)
#> Warning: coercing argument of type 'double' to logical
#> 
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