SYNOPSIS

DESCRIPTION

ImageDimension: 2 or 3 (for 2 or 3 dimensional operations). ImageDimension: 4 (for operations on 4D file, e.g. time-series data). Operator: See list of valid operators below. The last two arguments can be an image or float value NB: Some options output text files

Mathematical Operations:

m

: Multiply --- use vm for vector multiply

+

: Add --- use v+ for vector add

-

: Subtract --- use v- for vector subtract

/

: Divide

^

: Power

max

: voxelwise max

exp

: Take exponent exp(imagevalue*value)

addtozero

: add image-b to image-a only over points where image-a has zero values

overadd

: replace image-a pixel with image-b pixel if image-b pixel is non-zero

abs

: absolute value

total

: Sums up values in an image or in image1*image2 (img2 is the probability mask)

mean

: Average of values in an image or in image1*image2 (img2 is the probability mask)

vtotal

: Sums up volumetrically weighted values in an image or in image1*image2 (img2 is the probability mask)

Decision

: Computes result=1./(1.+exp(-1.0*( pix1-0.25)/pix2))

Neg

: Produce image negative

Spatial Filtering:

Project Image1.ext axis-a which-projection

: Project an image along axis a, which-projection=0(sum, 1=max, 2=min)

G Image1.ext s

: Smooth with Gaussian of sigma = s

MD Image1.ext s

: Morphological Dilation with radius s

ME Image1.ext s

: Morphological Erosion with radius s

MO Image1.ext s

: Morphological Opening with radius s

MC Image1.ext s

: Morphological Closing with radius s

GD Image1.ext s

: Grayscale Dilation with radius s

GE Image1.ext s

: Grayscale Erosion with radius s

GO Image1.ext s

: Grayscale Opening with radius s

GC Image1.ext s

: Grayscale Closing with radius s

BlobDetector Image1.ext NumberOfBlobsToExtract

Optional-Input-Image2 Blob-2-out.nii.gz N-Blobs-To-Match : blob detection by searching for local extrema of the Laplacian of the Gassian (LoG)

Example matching 6 best blobs from 2 images: ImageMath 2 blob.nii.gz BlobDetector image1.nii.gz 1000 image2.nii.gz blob2.nii.gz 6

MatchBlobs Image1.ext Image1LM.ext Image2.ext

Transform Image: Translate InImage.ext x [ y z ]

Time Series Operations:

CompCorrAuto : Outputs a csv file containing global signal vector and N comp-corr eigenvectors determined from PCA of the high-variance voxels.

Also outputs a comp-corr + global signal corrected 4D image as well as a 3D image measuring the time series variance. Requires a label image with label 1 identifying voxels in the brain.

ImageMath 4 ${out}compcorr.nii.gz ThreeTissueConfounds ${out}.nii.gz

${out}seg.nii.gz 1 3 : Outputs average global, CSF and WM signals. Requires a label image with 3 labels , csf, gm , wm .

Usage

: ThreeTissueConfounds 4D_TimeSeries.nii.gz LabeLimage.nii.gz csf-label wm-label

TimeSeriesSubset : Outputs n 3D image sub-volumes extracted uniformly from the input time-series 4D image.

Usage

: TimeSeriesSubset 4D_TimeSeries.nii.gz n

TimeSeriesDisassemble : Outputs n 3D image volumes for each time-point in time-series 4D image.

Usage

: TimeSeriesDisassemble 4D_TimeSeries.nii.gz

TimeSeriesAssemble : Outputs a 4D time-series image from a list of 3D volumes.

Usage

: TimeSeriesAssemble time_spacing time_origin *images.nii.gz

TimeSeriesToMatrix : Converts a 4D image + mask to matrix (stored as csv file) where rows are time and columns are space .

Usage

: TimeSeriesToMatrix 4D_TimeSeries.nii.gz mask

TimeSeriesSimpleSubtraction : Outputs a 3D mean pair-wise difference list of 3D volumes.

Usage

: TimeSeriesSimpleSubtraction image.nii.gz

TimeSeriesSurroundSubtraction : Outputs a 3D mean pair-wise difference list of 3D volumes.

Usage

: TimeSeriesSurroundSubtraction image.nii.gz

TimeSeriesSincSubtraction : Outputs a 3D mean pair-wise difference list of 3D volumes.

Usage

: TimeSeriesSincSubtraction image.nii.gz

SplitAlternatingTimeSeries : Outputs 2 3D time series

Usage

: SplitAlternatingTimeSeries image.nii.gz

ComputeTimeSeriesLeverage : Outputs a csv file that identifies the raw leverage and normalized leverage for each time point in the 4D image.

leverage, here, is the difference of the time-point image from the average of the n images. the normalized leverage is = average( sum_k abs(Leverage(t)-Leverage(k)) )/Leverage(t).

Usage

: ComputeTimeSeriesLeverage 4D_TimeSeries.nii.gz k_neighbors

SliceTimingCorrection : Outputs a slice-timing corrected 4D time series

Usage

: SliceTimingCorrection image.nii.gz sliceTiming [sinc / bspline] [sincRadius=4 / bsplineOrder=3]

PASL : computes the PASL model of CBF

f = rac{ lambda DeltaM }

{

2 alpha M_0 TI_1 exp( - TI_2 / T_{1a} ) }

Usage

: PASL 3D/4D_TimeSeries.nii.gz BoolFirstImageIsControl M0Image parameter_list.txt

pCASL : computes the pCASL model of CBF f = rac{ lambda DeltaM R_{1a} }

{

2 alpha M_0 [ exp( - w R_{1a} ) - exp( -w ( au + w ) R_{1a}) ] }

Usage

: pCASL 3D/4D_TimeSeries.nii.gz parameter_list.txt

PASLQuantifyCBF : Outputs a 3D CBF image in ml/100g/min from a magnetization ratio image

Usage

: PASLQuantifyCBF mag_ratio.nii.gz [TI1=700] [TI2=1900] [T1blood=1664] [Lambda=0.9] [Alpha=0.95] [SliceDelay-45]

Tensor Operations:

4DTensorTo3DTensor

: Outputs a 3D_DT_Image with the same information.

Usage

: 4DTensorTo3DTensor 4D_DTImage.ext

ComponentTo3DTensor

: Outputs a 3D_DT_Image with the same information as component images.

Usage

: ComponentTo3DTensor component_image_prefix[xx,xy,xz,yy,yz,zz] extension

ExtractComponentFrom3DTensor

: Outputs a component images.

Usage

: ExtractComponentFrom3DTensor dtImage.ext which={xx,xy,xz,yy,yz,zz}

ExtractVectorComponent: Produces the WhichVec component of the vector

Usage

: ExtractVectorComponent VecImage WhichVec

TensorColor

: Produces RGB values identifying principal directions

Usage

: TensorColor DTImage.ext

TensorFA

:

Usage

: TensorFA DTImage.ext

TensorFADenominator

:

Usage

: TensorFADenominator DTImage.ext

TensorFANumerator

:

Usage

: TensorFANumerator DTImage.ext

TensorIOTest

: Will write the DT image back out ... tests I/O processes for consistency.

Usage

: TensorIOTest DTImage.ext

TensorMeanDiffusion

: Mean of the eigenvalues

Usage

: TensorMeanDiffusion DTImage.ext

TensorRadialDiffusion

: Mean of the two smallest eigenvalues

Usage

: TensorRadialDiffusion DTImage.ext

TensorAxialDiffusion

: Largest eigenvalue, equivalent to TensorEigenvalue DTImage.ext 2

Usage

: TensorAxialDiffusion DTImage.ext

TensorEigenvalue

: Gets a single eigenvalue 0-2, where 0 = smallest, 2 = largest

Usage

: TensorEigenvalue DTImage.ext WhichInd

TensorToVector

: Produces vector field identifying one of the principal directions, 2 = largest eigenvalue

Usage

: TensorToVector DTImage.ext WhichVec

TensorToVectorComponent: 0 => 2 produces component of the principal vector field (largest eigenvalue). 3 = 8 => gets values from the tensor

Usage

: TensorToVectorComponent DTImage.ext WhichVec

TensorMask

: Mask a tensor image, sets background tensors to zero or to isotropic tensors with specified mean diffusivity

Usage

: TensorMask DTImage.ext mask.ext [ backgroundMD = 0 ]

FuseNImagesIntoNDVectorField

: Create ND field from N input scalar images

Usage

: FuseNImagesIntoNDVectorField imagex imagey imagez

Label Fusion:

MajorityVoting : Select label with most votes from candidates

Usage: MajorityVoting LabelImage1.nii.gz .. LabelImageN.nii.gz

CorrelationVoting : Select label with local correlation weights

Usage: CorrelationVoting Template.ext IntenistyImages* LabelImages* {Optional-Radius=5}

STAPLE : Select label using STAPLE method

Usage: STAPLE confidence-weighting LabelImages* Note: Gives probabilistic output (float)

MostLikely : Select label from from maximum probabilistic segmentations

Usage: MostLikely probabilityThreshold ProbabilityImages*

AverageLabels : Select label using STAPLE method

Usage: STAPLE LabelImages* Note: Gives probabilistic output (float)

Image Metrics & Info:

PearsonCorrelation: r-value from intesities of two images

Usage: PearsonCorrelation image1.ext image2.ext {Optional-mask.ext}

NeighborhoodCorrelation: local correlations

Usage: NeighborhoodCorrelation image1.ext image2.ext {Optional-radius=5} {Optional-image-mask}

NormalizedCorrelation: r-value from intesities of two images

Usage: NormalizedCorrelation image1.ext image2.ext {Optional-image-mask}

Demons:

Usage: Demons image1.ext image2.ext

Mattes: mutual information

Usage: Mattes image1.ext image2.ext {Optional-number-bins=32} {Optional-image-mask}

Unclassified Operators:

ReflectionMatrix : Create a reflection matrix about an axis

out.mat ReflectionMatrix image_in axis

MakeAffineTransform : Create an itk affine transform matrix ClosestSimplifiedHeaderMatrix : does what it says ... image-in, image-out Byte : Convert to Byte image in [0,255]

CompareHeadersAndImages: Tries to find and fix header errors. Outputs a repaired image with new header.

Never use this if you trust your header information.

Usage

: CompareHeadersAndImages Image1 Image2

ConvertImageSetToMatrix: Each row/column contains image content extracted from mask applied to images in *img.nii

Usage

: ConvertImageSetToMatrix rowcoloption Mask.nii *images.nii

ConvertImageSetToMatrix output can be an image type or csv file type.

RandomlySampleImageSetToCSV: N random samples are selected from each image in a list

Usage

: RandomlySampleImageSetToCSV N_samples *images.nii

RandomlySampleImageSetToCSV outputs a csv file type.

FrobeniusNormOfMatrixDifference: take the difference between two itk-transform matrices and then compute the frobenius norm

Usage

: FrobeniusNormOfMatrixDifference mat1 mat2

ConvertImageSetToEigenvectors: Each row/column contains image content extracted from mask applied to images in *img.nii

Usage

: ConvertImageSetToEigenvectors N_Evecs Mask.nii *images.nii

ConvertImageSetToEigenvectors output will be a csv file for each label value > 0 in the mask.

ConvertImageToFile

: Writes voxel values to a file

Usage

: ConvertImageToFile imagevalues.nii {Optional-ImageMask.nii}

ConvertLandmarkFile

: Converts landmark file between formats. See ANTS.pdf for description of formats.

Usage

: ConvertLandmarkFile InFile.txt

Example 1

: ImageMath 3 outfile.vtk ConvertLandmarkFile infile.txt

ConvertToGaussian

:

Usage

: ConvertToGaussian TValueImage sigma-float

ConvertVectorToImage

: The vector contains image content extracted from a mask. Here the vector is returned to its spatial origins as image content

Usage

: ConvertVectorToImage Mask.nii vector.nii

CorrelationUpdate

: In voxels, compute update that makes Image2 more like Image1.

Usage

: CorrelationUpdate Image1.ext Image2.ext RegionRadius

CountVoxelDifference

: The where function from IDL

Usage

: CountVoxelDifference Image1 Image2 Mask

CorruptImage

:

Usage

: CorruptImage Image NoiseLevel Smoothing

D

: Danielson Distance Transform

MaurerDistance : Maurer distance transform (much faster than Danielson)

Usage

: MaurerDistance inputImage {foreground=1}

DiceAndMinDistSum

: Outputs DiceAndMinDistSum and Dice Overlap to text log file + optional distance image

Usage

: DiceAndMinDistSum LabelImage1.ext LabelImage2.ext OptionalDistImage

EnumerateLabelInterfaces:

Usage

: EnumerateLabelInterfaces ImageIn ColoredImageOutname NeighborFractionToIgnore

ClusterThresholdVariate

: for sparse estimation

Usage

: ClusterThresholdVariate image mask MinClusterSize

ExtractSlice

: Extracts slice number from last dimension of volume (2,3,4) dimensions

Usage

: ExtractSlice volume.nii.gz slicetoextract

FastMarchingSegmentation: final output is the propagated label image. Optional stopping value: higher values allow more distant propagation

Usage

: FastMarchingSegmentation speed/binaryimagemask.ext initiallabelimage.ext Optional-Stopping-Value

FillHoles

: Parameter = ratio of edge at object to edge at background; --

Parameter = 1 is a definite hole bounded by object only, 0.99 is close Default of parameter > 1 will fill all holes

Usage

: FillHoles Image.ext parameter

InPaint

: very simple inpainting --- assumes zero values should be inpainted

Usage

: InPaint #iterations

PeronaMalik

: anisotropic diffusion w/varying conductance param (0.25 in example below)

Usage

: PeronaMalik image #iterations conductance

Convolve

: convolve input image with kernel image

Usage

: Convolve inputImage kernelImage {normalize=1}

Finite

: replace non-finite values with finite-value (default = 0)

Usage

: Finite Image.exdt {replace-value=0}

LabelSurfaceArea

:

Usage

: LabelSurfaceArea ImageIn {MaxRad-Default=1}

FlattenImage

: Replaces values greater than %ofMax*Max to the value %ofMax*Max

Usage

: FlattenImage Image %ofMax

GetLargestComponent

: Get the largest object in an image

Usage

: GetLargestComponent InputImage {MinObjectSize}

Grad

: Gradient magnitude with sigma s (if normalize, then output in range [0, 1])

Usage

: Grad Image.ext s normalize?

HistogramMatch

:

Usage

: HistogramMatch SourceImage ReferenceImage {NumberBins-Default=255} {NumberPoints-Default=64} {useThresholdAtMeanIntensity=false}

RescaleImage

:

Usage

: RescaleImage InputImage min max

WindowImage

:

Usage

: WindowImage InputImage windowMinimum windowMaximum outputMinimum outputMaximum

NeighborhoodStats

:

Usage

: NeighborhoodStats inputImage whichStat radius whichStat: 1 = min, 2 = max, 3 = variance, 4 = sigma, 5 = skewness, 6 = kurtosis, 7 = entropy

InvId

: computes the inverse-consistency of two deformations and write the inverse consistency error image

Usage

: InvId VectorFieldName VectorFieldName

ReplicateDisplacement

: replicate a ND displacement to a ND+1 image

Usage

: ReplicateDisplacement VectorFieldName TimeDims TimeSpacing TimeOrigin

ReplicateImage

: replicate a ND image to a ND+1 image

Usage

: ReplicateImage ImageName TimeDims TimeSpacing TimeOrigin

ShiftImageSlicesInTime

: shift image slices by one

Usage

: ShiftImageSlicesInTime ImageName shift-amount-default-1 shift-dim-default-last-dim

LabelStats

: Compute volumes / masses of objects in a label image. Writes to text file

Usage

: LabelStats labelimage.ext valueimage.nii

Laplacian

: Laplacian computed with sigma s (if normalize, then output in range [0, 1])

Usage

: Laplacian Image.ext s normalize?

Canny

: Canny edge detector

Usage

: Canny Image.ext sigma lowerThresh upperThresh

CropBinaryImage

: returns cropped image

Lipschitz

: Computes the Lipschitz norm of a vector field

Usage

: Lipschitz VectorFieldName

MakeImage

:

Usage

: MakeImage SizeX SizeY {SizeZ};

MTR

: Computes the magnetization transfer ratio ( (M0-M1)/M0 ) and truncates values to [0,1]

Usage

: MTR M0Image M1Image [MaskImage];

Normalize

: Normalize to [0,1]. Option instead divides by average value. If opt is a mask image, then we normalize by mean intensity in the mask ROI.

Usage

: Normalize Image.ext opt

PadImage

: If Pad-Number is negative, de-Padding occurs

Usage

: PadImage ImageIn Pad-Number

SigmoidImage

:

Usage

: SigmoidImage ImageIn [alpha=1.0] [beta=0.0]

Sharpen

:

Usage

: Sharpen ImageIn

CenterImage2inImage1

:

Usage

: ReferenceImageSpace ImageToCenter

PH

: Print Header

PoissonDiffusion

: Solves Poisson's equation in a designated region using non-zero sources

Usage

: PoissonDiffusion inputImage labelImage [sigma=1.0] [regionLabel=1] [numberOfIterations=500] [convergenceThreshold=1e-10]

PropagateLabelsThroughMask: Final output is the propagated label image. Optional stopping value: higher values allow more distant propagation

Usage

: PropagateLabelsThroughMask speed/binaryimagemask.nii.gz initiallabelimage.nii.gz Optional-Stopping-Value 0/1/2

0/1/2

=> 0, no topology constraint, 1 - strict topology constraint, 2 - no handles

PValueImage

:

Usage

: PValueImage TValueImage dof

RemoveLabelInterfaces:

Usage

: RemoveLabelInterfaces ImageIn

ReplaceVoxelValue: replace voxels in the range [a,b] in the input image with c

Usage

: ReplaceVoxelValue inputImage a b c

ROIStatistics

: computes anatomical locations, cluster size and mass of a stat image which should be in the same physical space (but not nec same resolution) as the label image.

Usage

: ROIStatistics LabelNames.txt labelimage.ext valueimage.nii

SetOrGetPixel

:

Usage

: SetOrGetPixel ImageIn Get/Set-Value IndexX IndexY {IndexZ}

Example 1

: ImageMath 2 outimage.nii SetOrGetPixel Image Get 24 34; Gets the value at 24, 34

Example 2

: ImageMath 2 outimage.nii SetOrGetPixel Image 1.e9 24 34; This sets 1.e9 as the value at 23 34

You can also pass a boolean at the end to force the physical space to be used

SetTimeSpacing

: sets spacing for last dimension

Usage

: SetTimeSpacing Image.ext tspacing

SetTimeSpacingWarp

: sets spacing for last dimension

Usage

: SetTimeSpacingWarp Warp.ext tspacing

stack

: Will put 2 images in the same volume

Usage

: Stack Image1.ext Image2.ext

ThresholdAtMean

: See the code

Usage

: ThresholdAtMean Image %ofMean

TileImages

:

Usage

: TileImages NumColumns ImageList*

TriPlanarView

:

Usage

: TriPlanarView ImageIn.nii.gz PercentageToClampLowIntensity PercentageToClampHiIntensity x-slice y-slice z-slice

TruncateImageIntensity:

Usage

: TruncateImageIntensity InputImage.ext {lowerQuantile=0.05} {upperQuantile=0.95} {numberOfBins=65} {binary-maskImage}

Where

: The where function from IDL

Usage

: Where Image ValueToLookFor maskImage-option tolerance