Difference between revisions of "Modules:ResampleVolume-Documentation-3.4"
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− | + | [[Documentation-3.4|Return to Slicer 3.4 Documentation]] | |
+ | |||
+ | [[Announcements:Slicer3.4#Highlights|Gallery of New Features]] | ||
__NOTOC__ | __NOTOC__ | ||
===Module Name=== | ===Module Name=== | ||
− | + | Resample Volume | |
{| | {| | ||
Line 15: | Line 17: | ||
Type: CLI | Type: CLI | ||
− | Category: | + | Category: Filtering |
===Authors, Collaborators & Contact=== | ===Authors, Collaborators & Contact=== | ||
Line 22: | Line 24: | ||
===Module Description=== | ===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 == | == Usage == | ||
Line 57: | Line 72: | ||
===Source code & documentation=== | ===Source code & documentation=== | ||
− | Source Code: [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/] | + | Source Code: [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/ResampleVolume.cxx?view=annotate ResampleVolume.cxx] |
+ | |||
+ | XML Description: [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/ResampleVolume.xml?view=co ResampleVolume.xml] | ||
+ | |||
+ | Usage: | ||
+ | <pre> | ||
+ | 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. | ||
+ | |||
+ | </pre> | ||
== More Information == | == More Information == | ||
Line 71: | Line 153: | ||
===References=== | ===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.[http://www.imagescience.org/meijering/publications/download/miccai1999.pdf (pdf)] |
Latest revision as of 20:12, 26 May 2009
Home < Modules:ResampleVolume-Documentation-3.4Return to Slicer 3.4 Documentation
Module Name
Resample Volume
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
List all the panels in your interface, their features, what they mean, and how to use them. For instance:
- 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)