Difference between revisions of "Modules:RicianLMMSEImageFilter-Documentation-3.6"
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===Module Type & Category=== | ===Module Type & Category=== | ||
− | Type: | + | Type: CLI |
− | Category: | + | Category: Diffusion MRI Applications |
===Authors, Collaborators & Contact=== | ===Authors, Collaborators & Contact=== | ||
Line 19: | Line 19: | ||
===Module Description=== | ===Module Description=== | ||
− | This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. Images | + | This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. Images corresponding to each gradient direction, including baseline, are processed individually. The noise parameter is automatically estimated (noise estimation improved but slower). |
+ | |||
+ | Note that this is a general purpose filter for MRi images. The module jointLMMSE has been specifically designed for DWI volumes and shows a better performance, so its use is recommended instead. | ||
+ | |||
+ | A complete description of the algorithm in this module can be found in: | ||
+ | |||
+ | S. Aja-Fernández, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin. "Restoration of DWI data using a Rician LMMSE estimator". IEEE Transactions on Medical Imaging, 27(10): pp. 1389-1403, Oct. 2008. | ||
== Usage == | == Usage == | ||
===Examples, Use Cases & Tutorials=== | ===Examples, Use Cases & Tutorials=== | ||
+ | |||
+ | A brief description of the most important parameters is given below. | ||
+ | |||
+ | Before filtering: | ||
+ | {| | ||
+ | |[[Image:orig.png|thumb|600px|Original unfiltered image]] | ||
+ | |} | ||
+ | |||
+ | After filtering: | ||
+ | |||
+ | {| | ||
+ | |[[Image:LMMSE.png|thumb|600px|Original unfiltered image]] | ||
+ | |} | ||
===Quick Tour of Features and Use=== | ===Quick Tour of Features and Use=== | ||
Line 47: | Line 66: | ||
===Source code & documentation=== | ===Source code & documentation=== | ||
+ | Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/dwiNoiseFilter link] | ||
+ | |||
+ | Doxygen documentation: | ||
+ | *[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1ComputeRestrictedHistogram.html itkComputeRestrictedHistogram.h] | ||
+ | *[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1ComputeStatisticsWherePositiveFilter.html itkComputeStatisticsWherePositiveFilter.h] | ||
+ | *[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1ExtractVolumeFilter.html itkExtractVolumeFilter.h] | ||
+ | *[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1LMMSEVectorImageFilter.html itkLMMSEVectorImageFilter.h] | ||
+ | *[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1LMMSEVectorImageFilterStep.html itkLMMSEVectorImageFilterStep.h] | ||
+ | *[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1MaskedMeanImageFilter.html itkMaskedMeanImageFilter.h] | ||
+ | *[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1VectorImageCastFilter.html itkVectorImageCastFilter.h] | ||
== More Information == | == More Information == | ||
===Acknowledgment=== | ===Acknowledgment=== | ||
− | Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially | + | Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially funded by grant number TEC2007-67073/TCM from the Ministerio de Ciencia e Innovación (Spain) and "FEDER" European Regional Development Fund. |
===References=== | ===References=== |
Latest revision as of 08:28, 17 May 2010
Home < Modules:RicianLMMSEImageFilter-Documentation-3.6Return to Slicer 3.6 Documentation
Module Name
jointLMMSE
General Information
Module Type & Category
Type: CLI
Category: Diffusion MRI Applications
Authors, Collaborators & Contact
- Author: Antonio Tristán Vega and Santiago Aja Fernández
- Contact: atriveg@bwh.harvard.edu
Module Description
This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. Images corresponding to each gradient direction, including baseline, are processed individually. The noise parameter is automatically estimated (noise estimation improved but slower).
Note that this is a general purpose filter for MRi images. The module jointLMMSE has been specifically designed for DWI volumes and shows a better performance, so its use is recommended instead.
A complete description of the algorithm in this module can be found in:
S. Aja-Fernández, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin. "Restoration of DWI data using a Rician LMMSE estimator". IEEE Transactions on Medical Imaging, 27(10): pp. 1389-1403, Oct. 2008.
Usage
Examples, Use Cases & Tutorials
A brief description of the most important parameters is given below.
Before filtering:
After filtering:
Quick Tour of Features and Use
It is very easy to use it. Just select a DWI, set the parameters (if you really need it), and you're ready to go.
- Input DWI Volume: set the DWI volume
- Output DWI Volume: the filtered DWI volume
- Estimation radius: This is the 3D radius of the neighborhood used for noise estimation. Noise power is estimated as the mode of the histogram of local variances
- Filtering radius: This is the 3D radius of the neighborhood used for filtering: local means and covariance matrices are estimated within this neighborhood
Development
Dependencies
Volumes. Needed to load DWI volumes
Known bugs
Usability issues
Source code & documentation
Source Code: Follow this link
Doxygen documentation:
- itkComputeRestrictedHistogram.h
- itkComputeStatisticsWherePositiveFilter.h
- itkExtractVolumeFilter.h
- itkLMMSEVectorImageFilter.h
- itkLMMSEVectorImageFilterStep.h
- itkMaskedMeanImageFilter.h
- itkVectorImageCastFilter.h
More Information
Acknowledgment
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially funded by grant number TEC2007-67073/TCM from the Ministerio de Ciencia e Innovación (Spain) and "FEDER" European Regional Development Fund.