ImageMath(1)

part of ANTS registration suite

NAME

ImageMath - part of ANTS registration suite

SYNOPSIS

ImageMath ImageDimension <OutputImage.ext> [operations and inputs] <Image1.ext> <Image2.ext>

DESCRIPTION

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)

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

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

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

FitSphere: :

Usage: : FitSphere GM-ImageIn {WM-Image} {MaxRad-Default=5}

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}

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 signa 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

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

Segment: : Segment an Image with option of Priors, weight 1 => maximally local/prior-based )

Usage: : Segment Image1.ext N-Classes LocalityVsGlobalityWeight-In-ZeroToOneRange OptionalPriorImages

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

History

Debian 9 (Stretch): the text shown here
Debian 8 (Jessie)
Debian 7 (Wheezy)
Debian 6.0 (Squeeze)