Usage Information 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(imagevaluevalue) 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 image1image2 (img2 is the probability mask) mean : Average of values in an image or in image1image2 (img2 is the probability mask) vtotal : Sums up volumetrically weighted values in an image or in image1image2 (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_raants.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: AverageLabels 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 %ofMaxMax to the value %ofMaxMax 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

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 PaddingSize [PaddingVoxelValue=0]

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

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