Provided by: ants_2.2.0-1ubuntu1_amd64 bug


       ImageMath - part of ANTS registration suite


       ImageMath   ImageDimension   <OutputImage.ext>   [operations   and   inputs]  <Image1.ext>


       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(imagevalue*value)

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

              : 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)

              : 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

       Usage  : TimeSeriesSimpleSubtraction image.nii.gz

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

       Usage  : TimeSeriesSurroundSubtraction image.nii.gz

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

       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))

       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 /

              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

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

   Tensor Operations:
              : Outputs a 3D_DT_Image with the same information.

       Usage  : 4DTensorTo3DTensor 4D_DTImage.ext

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

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

              : 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

              : Produces RGB values identifying principal directions

       Usage  : TensorColor DTImage.ext


       Usage  : TensorFA DTImage.ext


       Usage  : TensorFADenominator DTImage.ext


       Usage  : TensorFANumerator DTImage.ext

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

       Usage  : TensorIOTest DTImage.ext

              : Mean of the eigenvalues

       Usage  : TensorMeanDiffusion DTImage.ext

              : Mean of the two smallest eigenvalues

       Usage  : TensorRadialDiffusion DTImage.ext

              : Largest eigenvalue, equivalent to TensorEigenvalue DTImage.ext 2

       Usage  : TensorAxialDiffusion DTImage.ext

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

       Usage  : TensorEigenvalue DTImage.ext WhichInd

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

       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

              : 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 ]

              : 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*

              STAPLE : Select label using STAPLE method

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

              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}

              NormalizedCorrelation: r-value from intesities of two images

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


              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

       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.

              : Writes voxel values to a file

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

              : 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


       Usage  : ConvertToGaussian  TValueImage  sigma-float

              :  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

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

       Usage  : CorrelationUpdate Image1.ext Image2.ext RegionRadius

              : The where function from IDL

       Usage  : CountVoxelDifference Image1 Image2 Mask


       Usage  : CorruptImage Image NoiseLevel Smoothing

       D      : Danielson Distance Transform

              MaurerDistance : Maurer distance transform (much faster than Danielson)

       Usage  : MaurerDistance inputImage {foreground=1}

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

       Usage  : DiceAndMinDistSum LabelImage1.ext LabelImage2.ext OptionalDistImage


       Usage  : EnumerateLabelInterfaces ImageIn ColoredImageOutname NeighborFractionToIgnore

              :  for sparse estimation

       Usage  : ClusterThresholdVariate image mask  MinClusterSize

              : 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

              : 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

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

       Usage  : InPaint #iterations

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

       Usage  : PeronaMalik image #iterations conductance

              : 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}


       Usage  : LabelSurfaceArea ImageIn {MaxRad-Default=1}

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

       Usage  : FlattenImage Image %ofMax

              : 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?


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


       Usage  : RescaleImage InputImage min max


       Usage  : WindowImage InputImage windowMinimum windowMaximum outputMinimum outputMaximum


       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

              : replicate a ND displacement to a ND+1 image

       Usage  : ReplicateDisplacement VectorFieldName TimeDims TimeSpacing TimeOrigin

              : replicate a ND image to a ND+1 image

       Usage  : ReplicateImage ImageName TimeDims TimeSpacing TimeOrigin

              : shift image slices by one

       Usage  :          ShiftImageSlicesInTime          ImageName         shift-amount-default-1

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

       Usage  : LabelStats labelimage.ext valueimage.nii

              : 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

              : Computes the Lipschitz norm of a vector field

       Usage  : Lipschitz VectorFieldName


       Usage  : MakeImage SizeX  SizeY {SizeZ};

       MTR    : Computes the magnetization transfer ratio ( (M0-M1)/M0 ) and truncates values  to

       Usage  : MTR M0Image M1Image [MaskImage];

              :  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

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

       Usage  : PadImage ImageIn Pad-Number


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


       Usage  : Sharpen ImageIn


       Usage  : ReferenceImageSpace ImageToCenter

       PH     : Print Header

              : 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


       Usage  : PValueImage TValueImage dof


       Usage  : RemoveLabelInterfaces ImageIn

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

       Usage  : ReplaceVoxelValue inputImage a b c

              : 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


       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

              : sets spacing for last dimension

       Usage  : SetTimeSpacing Image.ext tspacing

              : 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

              : See the code

       Usage  : ThresholdAtMean Image %ofMean


       Usage  : TileImages NumColumns ImageList*


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


       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