ImageMath: Image Math

In stnava/itkImageR: Examples using Rcpp to interface R and ITK

Description

Perform math-operations on the given image

Usage

ImageMath( "<ImageDimension>" , "<OutputImage>" , "<operator>" , "<Image1.ext>" [, "<Image2.ext>"] )
See section 'Arguments' for details. Number and type of arguments vary depending on 'operator' used.

Arguments

imageDimension 2|3	Number of dimensions of the input image
outputImage	result image
operator	Must be one of the operators listed below. Mathematical Operations: m : Multiply + : Add - : Subtract / : Divide ^ : Power 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) Decision : Computes result=1./(1.+exp(-1.0*( pix1-0.25)/pix2)) Neg : Produce image negative Spatial Filtering: G : Smooth with Gaussian of sigma = s Usage: G Image1.ext s MD : Morphological Dilation with radius s Usage: MD Image1.ext s ME : Morphological Erosion with radius s Usage: ME Image1.ext s MO : Morphological Opening with radius s Usage: MO Image1.ext s MC : Morphological Closing with radius s Usage: MC Image1.ext s GD : Grayscale Dilation with radius s Usage: GD Image1.ext s GE : Grayscale Erosion with radius s Usage: GE Image1.ext s GO : Grayscale Opening with radius s Usage: GO Image1.ext s GC : Grayscale Closing with radius s Usage: GC Image1.ext s 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. Usage: CompCorrAuto 4D_TimeSeries.nii.gz LabeLimage.nii.gz N-comp-corr-eigenvectors CompCorr : Outputs a comp-corr corrected 4D image as well as a 3D image measuring the correlation of a time series voxel/region with a reference voxel/region factored out. Requires a label image with 1=overall region of interest, 2=reference voxel, 3=region to factor out. If there is no 3rd label, then only the global signal is factored out. Usage: CompCorr 4D_TimeSeries.nii.gz LabeLimage.nii.gz Sigma-for-temporal-smoothing TimeSeriesSubset : Outputs n 3D image sub-volumes extracted uniformly from the input time-series 4D image. Usage: TimeSeriesSubset 4D_TimeSeries.nii.gz n 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 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 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 : Tensor FA Usage: TensorFA DTImage.ext TensorFADenominator : Tensor FA denominator Usage: TensorFADenominator DTImage.ext TensorFANumerator : Tensor FA numerator Usage: TensorFANumerator DTImage.ext TensorIOTest : Will write the DT image back out ... tests I/O processes for consistency. Usage: TensorIOTest DTImage.ext TensorMeanDiffusion : Tensor mean diffusion Usage: TensorMeanDiffusion DTImage.ext 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 Unclassified Operators: 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. ConvertImageSetToMatrix output can be an image type or csv file type. Usage: ConvertImageSetToMatrix rowcoloption Mask.nii *images.nii RandomlySampleImageSetToCSV: N random samples are selected from each image in a list. RandomlySampleImageSetToCSV outputs a csv file type. Usage: RandomlySampleImageSetToCSV N_samples *images.nii ConvertImageSetToEigenvectors: Each row/column contains image content extracted from mask applied to images in *img.nii. ConvertImageSetToEigenvectors output will be a csv file for each label value > 0 in the mask. Usage: ConvertImageSetToEigenvectors N_Evecs Mask.nii *images.nii 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 : Convert to Gaussian 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 : Currupt image Usage: CorruptImage Image NoiseLevel Smoothing D : DistanceTransform DiceAndMinDistSum : Outputs DiceAndMinDistSum and Dice Overlap to text log file + optional distance image Usage: DiceAndMinDistSum LabelImage1.ext LabelImage2.ext OptionalDistImage EnumerateLabelInterfaces: Enumerate label interfaces Usage: EnumerateLabelInterfaces ImageIn ColoredImageOutname NeighborFractionToIgnore 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 FitSphere : Fit sphere Usage: FitSphere GM-ImageIn WM-Image MaxRad-Default=5 FlattenImage : Replaces values greater than percentageofMax*Max to the value percentageofMax*Max Usage: FlattenImage Image percentageofMax 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 : Histogram matching Usage: HistogramMatch SourceImage ReferenceImage NumberBins-Default=255 NumberPoints-Default=64 InvId : computes the inverse-consistency of two deformations and write the inverse consistency error image Usage: InvId VectorFieldName VectorFieldName 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? Lipschitz : Computes the Lipschitz norm of a vector field Usage: Lipschitz VectorFieldName MakeImage : Make Image Usage: MakeImage SizeX SizeY SizeZ; 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 CenterImage2inImage1 : Center image2 to image1 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 PValueImage : P value image Usage: PValueImage TValueImage dof RemoveLabelInterfaces: Remove label interfaces Usage: RemoveLabelInterfaces ImageIn 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 : Set or get pixel; You can also pass a boolean at the end to force the physical space to be used 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 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 percentageofMean TileImages : Tile Images Usage: TileImages NumColumns ImageList TriPlanarView : Tri planar view Usage: TriPlanarView ImageIn.nii.gz PercentageToClampLowIntensity PercentageToClampHiIntensity x-slice y-slice z-slice TruncateImageIntensity: Truncate image intensity 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

Value

0 – Success
1 – Failure

Author(s)

Shrinidhi KL

Examples

## Not run: 
ImageMath( 3 , "output_img.nii" , "D", "input_img.nii" , 1 )
ImageMath( 3 , "output_img.nii" , "MD", "input_img.nii" , 1 )

## End(Not run)

stnava/itkImageR documentation built on May 30, 2019, 7:21 p.m.