Modules:ResampleVolume-Documentation-3.4

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Module Name

Resample Volume

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General Information

Module Type & Category

Type: CLI

Category: Filtering

Authors, Collaborators & Contact

• Author: Bill Lorensen
• Contact: bill.lorensen at gmail.com

Module Description

Resampling an image is an important task in image analysis.It is especially important in the frame of image registration. This module implements image resampling through the use of itk Transforms. This module uses an Identity Transform. The resampling is controlled by the Output Spacing. 'Resampling' is performed in space coordinates, not pixel/grid coordinates. It is quite important to ensure that image spacing is properly set on the images involved. The interpolator is required since the mapping from one space to the other will often require evaluation of the intensity of the image at non-grid positions. Several interpolators are available: linear, nearest neighbor and five flavors of sinc. The sinc interpolators, although more precise, are much slower than the linear and nearest neighbor interpolator. To resample label volumnes, nearest neighbor interpolation should be used exclusively.

Usage

Examples, Use Cases & Tutorials

• Note use cases for which this module is especially appropriate, and/or link to examples.
• Link to examples of the module's use
• Link to any existing tutorials

Quick Tour of Features and Use

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• Input panel:
• Parameters panel:
• Output panel:
• Viewing panel:

Development

Dependencies

Other modules or packages that are required for this module's use.

Known bugs

Follow this link to the Slicer3 bug tracker.

Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

Source Code: ResampleVolume.cxx

XML Description: ResampleVolume.xml

Usage:

ResampleVolume  [--processinformationaddress
                <std::string>] [--xml] [--echo] [-i
                <linear|nearestNeighbor|hamming
                |cosine|welch|lanczos|blackman>]
                [-s <std::vector<float>>] [--]
                [--version] [-h] <std::string>
                <std::string>
Where: 
   --processinformationaddress <std::string>
     Address of a structure to store process information (progress, abort,
     etc.). (default: 0)

   --xml
     Produce xml description of command line arguments (default: 0)

   --echo
     Echo the command line arguments (default: 0)

   -i <linear|nearestNeighbor|hamming|cosine|welch|lanczos|blackman>, 
      --interpolation <linear|nearestNeighbor|hamming|cosine|welch|lanczos
      |blackman>
     Sampling algorithm (linear, nearest neighbor  or windowed sinc). There
     are several sinc algorithms available as described in the following
     publication: Erik H. W. Meijering, Wiro J. Niessen, Josien P. W. Pluim
     , Max A. Viergever: Quantitative Comparison of Sinc-Approximating
     Kernels for Medical Image Interpolation. MICCAI 1999, pp. 210-217.
     Each window has a radius of 3; (default: linear)

   -s <std::vector<float>>,  --spacing <std::vector<float>>
     Spacing along each dimension (0 means use input spacing) (default: 0,0
     ,0)

   --,  --ignore_rest
     Ignores the rest of the labeled arguments following this flag.

   --version
     Displays version information and exits.

   -h,  --help
     Displays usage information and exits.

   <std::string>
     (required)  Input volume to be resampled

   <std::string>
     (required)  Resampled Volume


   Description: Resampling an image is an important task in image
   analysis.It is especially important in the frame of image registration.
   This module implements image resampling through the use of itk
   Transforms. This module uses an Identity Transform. The resampling is
   controlled by the Output Spacing. 'Resampling' is performed in space
   coordinates, not pixel/grid coordinates. It is quite important to ensure
   that image spacing is properly set on the images involved. The
   interpolator is required since the mapping from one space to the other
   will often require evaluation of the intensity of the image at non-grid
   positions. Several interpolators are available: linear, nearest neighbor
   and five flavors of sinc. The sinc interpolators, although more precise,
   are much slower than the linear and nearest neighbor interpolator. To
   resample label volumnes, nearest neighbor interpolation should be used
   exclusively.

More Information

Acknowledgment

This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from National Centers for Biomedical Computing.

References

Erik H. W. Meijering, Wiro J. Niessen, Josien P. W. Pluim, Max A. Viergever: Quantitative Comparison of Sinc-Approximating Kernels for Medical Image Interpolation. MICCAI 1999, pp. 210-217.(pdf)