Slicer Wiki - User contributions [en]

Modules:RicianLMMSEImageFilter-Documentation-3.6

2010-05-13T14:53:49Z

Atriveg: /* Examples, Use Cases & Tutorials */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===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:

[[File:orig.png]]

After filtering:

[[File:LMMSE.png]]

===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 [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 ==

===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.

===References===

Modules:RicianLMMSEImageFilter-Documentation-3.6

2010-05-13T14:52:30Z

Atriveg: /* Module Description */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===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===

===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 [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 ==

===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.

===References===

Modules:UnbiasedNonLocalMeans-Documentation-3.6

2010-05-13T14:48:18Z

Atriveg: /* References */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===Module Name===
UnbiasedNonLocalMeans

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , 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 images using a Unbiased Non Local Means for Rician noise algorithm. It exploits not only the spatial redundancy, but the redundancy in similar gradient directions as well; it takes into account the N closest gradient directions to the direction being processed (a maximum of 5 gradient directions is allowed to keep a reasonable computational load, since we do not use neither similarity maps nor block-wise implementation).

The noise parameter is automatically estimated in the same way as in the jointLMMSE module.

A complete description of the algorithm may be found in:

Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.

Please, note that the execution of this filter is extremely slow, son only very conservative parameters (block size and search size as small as possible) should be used. Even so, its execution may take several hours. The advantage of this filter over joint LMMSE is its better preservation of edges and fine structures.

== Usage ==

===Examples, Use Cases & Tutorials===

To use this filter, just load a DWI volume (gradient directions must be known) and apply the filter, as illustrated below. A brief summary of the parameters used is given below.

Before filtering:

[[File:orig.png]]

After filtering:

[[File:UNLM.png]]

===Quick Tour of Features and Use===
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
* '''Search radius:''' This filter works by computing the weighted average of pixels in a large neighborhood. The search radius is the 3D radius of that neighborhood.
* '''Comparison radius:''' The weights of the average are computed as the negative exponential of the (normalized) distance between two neighborhoods which have a 3D radius of this size.
* '''h:''' The normalization constant for the distance between neighborhoods. This parameter is related to the noise power, and should be in the range 0.8->1.2. Higher values produce more blurring (and more noise reduction), while lower values better preserve edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. It is strongly recommended to keep this parameter less than 5 to keep a reasonable computational load. Even so, this filter is extremely slow.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

This filter is very slow. May take hours.

===Source code & documentation===
Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/dwiUNLM 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_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1UNLMFilter.html itkUNLMFilter.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.

===References===

* Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218 (2010).
* Santiago Aja-Fernández, Antonio Tristán-Vega, and Carlos Alberola-López, "Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models". Magnetic Resonance Imaging, Volume 27, Pages 1397-1409 (2009).

Modules:UnbiasedNonLocalMeans-Documentation-3.6

2010-05-13T14:47:27Z

Atriveg: /* Module Description */

Modules:UnbiasedNonLocalMeans-Documentation-3.6

2010-05-13T14:46:59Z

Atriveg: /* Examples, Use Cases & Tutorials */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===Module Name===
UnbiasedNonLocalMeans

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , 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 images using a Unbiased Non Local Means for Rician noise algorithm. It exploits not only the spatial redundancy, but the redundancy in similar gradient directions as well; it takes into account the N closest gradient directions to the direction being processed (a maximum of 5 gradient directions is allowed to keep a reasonable computational load, since we do not use neither similarity maps nor block-wise implementation). The noise parameter is automatically estimated.

A complete description of the algorithm may be found in:

Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.

Please, note that the execution of this filter is extremely slow, son only very conservative parameters (block size and search size as small as possible) should be used. Even so, its execution may take several hours. The advantage of this filter over joint LMMSE is its better preservation of edges and fine structures.

== Usage ==

===Examples, Use Cases & Tutorials===

To use this filter, just load a DWI volume (gradient directions must be known) and apply the filter, as illustrated below. A brief summary of the parameters used is given below.

Before filtering:

[[File:orig.png]]

After filtering:

[[File:UNLM.png]]

===Quick Tour of Features and Use===
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
* '''Search radius:''' This filter works by computing the weighted average of pixels in a large neighborhood. The search radius is the 3D radius of that neighborhood.
* '''Comparison radius:''' The weights of the average are computed as the negative exponential of the (normalized) distance between two neighborhoods which have a 3D radius of this size.
* '''h:''' The normalization constant for the distance between neighborhoods. This parameter is related to the noise power, and should be in the range 0.8->1.2. Higher values produce more blurring (and more noise reduction), while lower values better preserve edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. It is strongly recommended to keep this parameter less than 5 to keep a reasonable computational load. Even so, this filter is extremely slow.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

This filter is very slow. May take hours.

===Source code & documentation===
Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/dwiUNLM 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_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1UNLMFilter.html itkUNLMFilter.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.

===References===

Modules:UnbiasedNonLocalMeans-Documentation-3.6

2010-05-13T14:45:07Z

Atriveg: /* Module Description */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===Module Name===
UnbiasedNonLocalMeans

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , 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 images using a Unbiased Non Local Means for Rician noise algorithm. It exploits not only the spatial redundancy, but the redundancy in similar gradient directions as well; it takes into account the N closest gradient directions to the direction being processed (a maximum of 5 gradient directions is allowed to keep a reasonable computational load, since we do not use neither similarity maps nor block-wise implementation). The noise parameter is automatically estimated.

A complete description of the algorithm may be found in:

Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.

Please, note that the execution of this filter is extremely slow, son only very conservative parameters (block size and search size as small as possible) should be used. Even so, its execution may take several hours. The advantage of this filter over joint LMMSE is its better preservation of edges and fine structures.

== Usage ==

===Examples, Use Cases & Tutorials===

===Quick Tour of Features and Use===
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
* '''Search radius:''' This filter works by computing the weighted average of pixels in a large neighborhood. The search radius is the 3D radius of that neighborhood.
* '''Comparison radius:''' The weights of the average are computed as the negative exponential of the (normalized) distance between two neighborhoods which have a 3D radius of this size.
* '''h:''' The normalization constant for the distance between neighborhoods. This parameter is related to the noise power, and should be in the range 0.8->1.2. Higher values produce more blurring (and more noise reduction), while lower values better preserve edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. It is strongly recommended to keep this parameter less than 5 to keep a reasonable computational load. Even so, this filter is extremely slow.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

This filter is very slow. May take hours.

===Source code & documentation===
Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/dwiUNLM 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_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1UNLMFilter.html itkUNLMFilter.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.

===References===

Modules:JointRicianLMMSEImageFilter-Documentation-3.6

2010-05-13T14:41:57Z

Atriveg: /* Examples, Use Cases & Tutorials */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===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 Rician 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. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-diemensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. To that end, the covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process.

The noise parameter is automatically estimated from a rough segmentation of the background of the image. In this area the signal is simply 0, so that Rician statistics reduce to Rayleigh and the noise power can be easily estimated from the mode of the histogram.

A complete description of the algorithm may be found in:

Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.

== Usage ==

The filter operates on an input DWI volume (gradient directions must be known). The meaning of the parameters of this module is listed below.

===Examples, Use Cases & Tutorials===
The following screenshots illustrate how to use the filter and the expected results (note that the optimal parameters strongly depends on the characteristics of the image: voxel size, number of gradients...).

Before filtering:

[[File:Orig.png]]

After filtering:

[[File:jLMMSE.png]]

===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. A large radius more effectively reduces the noise but may induce a certain blurring of the edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.
** NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/jointLMMSE 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_1LMMSEVectorImageFilter.html itkLMMSEVectorImageFilter.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.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.

===References===

* Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218 (2010).
* Santiago Aja-Fernández, Antonio Tristán-Vega, and Carlos Alberola-López, "Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models". Magnetic Resonance Imaging, Volume 27, Pages 1397-1409 (2009).

Modules:JointRicianLMMSEImageFilter-Documentation-3.6

2010-05-13T14:41:15Z

Atriveg: /* Examples, Use Cases & Tutorials */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===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 Rician 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. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-diemensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. To that end, the covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process.

The noise parameter is automatically estimated from a rough segmentation of the background of the image. In this area the signal is simply 0, so that Rician statistics reduce to Rayleigh and the noise power can be easily estimated from the mode of the histogram.

A complete description of the algorithm may be found in:

Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.

== Usage ==

The filter operates on an input DWI volume (gradient directions must be known). The meaning of the parameters of this module is listed below.

===Examples, Use Cases & Tutorials===
The following screenshots illustrate how to use the filter and the expected results (note that the optimal parameters strongly depends on the characteristics of the image: voxel size, number of gradients...).

Before filtering:

[[File:Orig.png]]

After filtering:
[[File:jLMMSE.png]]

===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. A large radius more effectively reduces the noise but may induce a certain blurring of the edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.
** NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/jointLMMSE 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_1LMMSEVectorImageFilter.html itkLMMSEVectorImageFilter.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.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.

===References===

* Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218 (2010).
* Santiago Aja-Fernández, Antonio Tristán-Vega, and Carlos Alberola-López, "Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models". Magnetic Resonance Imaging, Volume 27, Pages 1397-1409 (2009).

File:UNLM.png

2010-05-13T14:39:22Z

Atriveg:

File:LMMSE.png

2010-05-13T14:38:54Z

Atriveg:

File:JLMMSE.png

2010-05-13T14:38:32Z

Atriveg:

File:Orig.png

2010-05-13T14:37:45Z

Atriveg:

Modules:UnbiasedNonLocalMeans-Documentation-3.6

2010-05-13T14:28:03Z

Atriveg: /* Acknowledgment */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===Module Name===
UnbiasedNonLocalMeans

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , 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 images using a Unbiased Non Local Means for Rician noise algorithm. It exploits not only the spatial redundancy, but the redundancy in similar gradient directions as well; it takes into account the N closest gradient directions to the direction being processed (a maximum of 5 gradient directions is allowed to keep a reasonable computational load, since we do not use neither similarity maps nor block-wise implementation). The noise parameter is automatically estimated. A complete description of the algorithm may be found in "DWI filtering using joint information for DTI and HARDI", Antonio Tristan Vega and Santiago Aja-Fernandez, Medical Image Analysis, Volume 14, Issue 2, Pages 205-218.

== Usage ==

===Examples, Use Cases & Tutorials===

===Quick Tour of Features and Use===
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
* '''Search radius:''' This filter works by computing the weighted average of pixels in a large neighborhood. The search radius is the 3D radius of that neighborhood.
* '''Comparison radius:''' The weights of the average are computed as the negative exponential of the (normalized) distance between two neighborhoods which have a 3D radius of this size.
* '''h:''' The normalization constant for the distance between neighborhoods. This parameter is related to the noise power, and should be in the range 0.8->1.2. Higher values produce more blurring (and more noise reduction), while lower values better preserve edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. It is strongly recommended to keep this parameter less than 5 to keep a reasonable computational load. Even so, this filter is extremely slow.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

This filter is very slow. May take hours.

===Source code & documentation===
Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/dwiUNLM 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_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1UNLMFilter.html itkUNLMFilter.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.

===References===

Modules:RicianLMMSEImageFilter-Documentation-3.6

2010-05-13T14:27:40Z

Atriveg: /* Acknowledgment */

Modules:JointRicianLMMSEImageFilter-Documentation-3.6

2010-05-13T14:27:01Z

Atriveg: /* Acknowledgment */

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===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 Rician 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. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-diemensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. To that end, the covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process.

The noise parameter is automatically estimated from a rough segmentation of the background of the image. In this area the signal is simply 0, so that Rician statistics reduce to Rayleigh and the noise power can be easily estimated from the mode of the histogram.

A complete description of the algorithm may be found in:

Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.

== Usage ==

The filter operates on an input DWI volume (gradient directions must be known). The meaning of the parameters of this module is listed below.

===Examples, Use Cases & Tutorials===

===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. A large radius more effectively reduces the noise but may induce a certain blurring of the edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.
** NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/jointLMMSE 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_1LMMSEVectorImageFilter.html itkLMMSEVectorImageFilter.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.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.

===References===

* Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218 (2010).
* Santiago Aja-Fernández, Antonio Tristán-Vega, and Carlos Alberola-López, "Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models". Magnetic Resonance Imaging, Volume 27, Pages 1397-1409 (2009).

Modules:JointRicianLMMSEImageFilter-Documentation-3.6

2010-05-13T14:15:48Z

Atriveg:

[[Documentation-3.6|Return to Slicer 3.6 Documentation]]

[[Announcements:Slicer3.6#Highlights|Gallery of New Features]]
__NOTOC__
===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 Rician 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. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-diemensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. To that end, the covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process.

The noise parameter is automatically estimated from a rough segmentation of the background of the image. In this area the signal is simply 0, so that Rician statistics reduce to Rayleigh and the noise power can be easily estimated from the mode of the histogram.

A complete description of the algorithm may be found in:

Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.

== Usage ==

The filter operates on an input DWI volume (gradient directions must be known). The meaning of the parameters of this module is listed below.

===Examples, Use Cases & Tutorials===

===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. A large radius more effectively reduces the noise but may induce a certain blurring of the edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.
** NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

Source Code: Follow this [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/DiffusionApplications/jointLMMSE 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_1LMMSEVectorImageFilter.html itkLMMSEVectorImageFilter.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuStatistics.html itkOtsuStatistics.h]
*[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/classitk_1_1OtsuThreshold.html itkOtsuThreshold.h]

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

* Antonio Tristán-Vega and Santiago Aja-Fernández, "DWI filtering using joint information for DTI and HARDI", Medical Image Analysis, Volume 14, Issue 2, Pages 205-218 (2010).
* Santiago Aja-Fernández, Antonio Tristán-Vega, and Carlos Alberola-López, "Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models". Magnetic Resonance Imaging, Volume 27, Pages 1397-1409 (2009).

Documentation/3.4

2009-03-03T10:13:37Z

Atriveg: /* DWI */

Note: This page is currently under construction

=Introduction=
[[Image:Base-Features-and-Modules.png|thumb|right|overview|[[Media:Integrating with Slicer3.ppt | Integrating with Slicer3]]]]
This page is a portal for documentation about Slicer 3.4.
For information for software developers, please go to the Developers page (see link in navigation box to the left).

=How-To Tutorials=
[http://wiki.na-mic.org/Wiki/index.php/Slicer3:Training Slicer3 tutorial page]

=Feature Request and Problem Reports=
We have an [http://www.na-mic.org/Bug/my_view_page.php issues tracker] for Slicer 3. You need to create an account for filing reports. We keep track of both feature requests and bug reports. Make sure to use the pull-down in the upper right to select Slicer 3.

=List of Modules in need of documentation=
== Requirements for modules to be added to the release==
{| border="00" cellpadding="5" cellspacing="0"
|-
| rowspan="2"|
* The module is '''feature complete''' for the tasks advertised on 2-4-2009
* The module has a '''test'''. See [http://wiki.na-mic.org/Wiki/index.php/Slicer3:Execution_Model_Testing '''here'''] for more information.
* Module has '''documentation''' on the [[Documentation-3.4#Modules|Slicer wiki]]. Please use the template provided [[Documentation-3.4#Modules|'''here''']] to structure your page.
*Please add a pointer to the documentation on the Slicer wiki to the the '''Help''' tab of the module. See the '''Editor module''' in Slicer for an example.
* The contributor (and their manager/advisor), the lab (with labs/institution logo) and the funding source (with grant number, logo optional) are listed in the '''Acknowledgment''' tab of the module. Please see the '''Models module''' for and example. The people listed in the acknowledgement will be the primary people for support and maintenance relative of the module.
** '''Style Guide:''' All acknowledgment icons should be 100x100 pixels, preferably in png format.
** '''Accessing logos:''' Icons for BIRN, NAC, NA-MIC and IGT are included in Slicer3/Base/GUI//vtkSlicerBaseAcknowledgementLogoIcons.cxx/h and resources for them are in Slicer3/Base/GUI/Resources/vtkSlicerBaseAcknowledgementLogos_ImageData.h. The API for vtkSlicerModuleGUI provides access to these icons.
** '''Adding logos:''' Please add additional image resources and logo icons to these files as required in order to promote shared use (and to prevent duplication in the code.)
* If your module has [[Documentation-3.2|documentation in Slicer 3.2]], please copy/paste/update into the 3.4 version
| style="background: #e5e5e5" align="center"| Examples for the Help and
Acknowledgment Panels
|-
| style="background: #ebeced"|[[Image:SlicerHelpExample.png|center|200px]][[Image:SlicerAcknowledgementExample.png|center|200px]]
|}

==Main GUI==
*[[Modules:MainApplicationGUI-Documentation-3.4| Main Application GUI]] (Wendy Plesniak)
*[[Modules:EventBindings-3.4| List of Hotkeys and Keyboard Shortcuts]] (Wendy Plesniak)
*[[Modules:Loading-Data-3.4| How to load data]] (Steve Pieper)
*[[Modules:Saving-Documentation-3.4| Save Scene and Data Module]] (Wendy Plesniak)

==Modules==
*Please copy the template linked below, paste it into your page and customize it with your module's information.
[[Slicer3:Module_Documentation-3.4_Template|Slicer3:Module_Documentation-3.4_Template]]
*See above for info to be put into the Help and Acknowledgment Tabs
*To put your lab's logo into a module, see [[Slicer3:Execution_Model_Documentation#Adding_Module_Logos_to_Slicer3|here]]
===Core and Loadable Modules===
*[[Modules:Welcome-Documentation-3.4| Welcome Module]] (Wendy Plesniak, Steve Pieper, Sonia Pujol, Ron Kikinis)

*[[Modules:Volumes-Documentation-3.4| Volumes Module]] (Alex Yarmarkovich, Steve Pieper)
**[[Modules:Volumes:Diffusion Editor-Documentation-3.4| Diffusion Editor]] (Kerstin Kessel)
*[[Modules:Models-Documentation-3.4| Models Module]] (Alex Yarmarkovich)

*[[Modules:Fiducials-Documentation-3.4| Fiducials Module]] (Nicole Aucoin)
*[[Modules:Data-Documentation-3.4| Data Module]] (Alex Yarmarkovich)
*[[Modules:Slices-Documentation-3.4|Slices Module]] (Jim Miller)
*[[Modules:Transforms-Documentation-3.4| Transforms Module]] (Alex Yarmarkovich)
*[[Modules:Color-Documentation-3.4| Color Module]] (Nicole Aucoin)

*[[Modules:Editor-Documentation-3.4| Interactive Editor]] (Steve Pieper)
*[[Modules:ROIModule-Documentation-3.4|ROI Module]] (Alex Yarmarkovich)
*[[Modules:VolumeRendering-Documentation-3.4| Volume Rendering Module]] (Alex Yarmarkovich)

==Other Modules==

Please adhere to the naming scheme for the module documentation:
*[ [Modules:MyModuleNameNoSpaces-Documentation-3.4|My Module Name With Spaces] ] (First Last Name)
===Wizards===
*[[Modules:ChangeTracker-Documentation-3.4|ChangeTracker]] (Andriy Fedorov)
*[[Modules:IA_FEMesh-Documentation-3.4|IA FE Meshing Module]] (Vince Magnotta)
===Informatics Modules===
*[[Modules:FetchMI-Documentation-3.4| Fetch Medical Informatics Module]] (Wendy Plesniak)
*[[Modules:QDECModule-Documentation-3.4| QDEC Module]] (Nicole Aucoin)
*[[Modules:QueryAtlas-Documentation-3.4|Query Atlas Module]] (Wendy Plesniak)
===Registration===
*[[Modules:AffineRegistration-Documentation-3.4|Affine Registration]] (Daniel Blezek)
*[[Modules:DeformableB-SplineRegistration-Documentation-3.4|Deformable B-Spline Registration]] (Bill Lorensen)
*[[Modules:DiffeomorphicDemonsAlgorithm-Documentation3.4|Diffeomorphic Demons Algorithm]] (Tom Vercauteren, Ender Konukoglu, Kilian Pohl)
*[[Modules:LinearRegistration-Documentation-3.4|Linear Registration]] (Daniel Blezek)
*[[Modules:RealignVolume-Documentation-3.4|Realign Volume]] (Nicole Aucoin)
*[[Modules:RigidRegistration-Documentation-3.4|Rigid Registration]] (Daniel Blezek)
===Segmentation===
*EM Segment Command-Line (Brad Davis, Will Schroeder)
*EM Segment Simple (Brad Davis, Will Schroeder)
*EMSegmentTemplateBuilder (Brad Davis, Will Schroeder)
*[[Modules:Simple Region Growing-Documentation-3.4|Simple Region Growing]] (Jim Miller)
*[[Modules:OtsuThreshold-Documentation-3.4|Otsu Threshold]] (Bill Lorensen)
===Statistics===
*Calculate Volume Statistics (Tri Ngo)
*[[Modules:LabelStatistics-Documentation-3.4|Label Statistics]] (Steve Pieper)
===DWI and Tractography===
====DWI====
*Estimation
**[[Modules:DiffusionTensorEstimation-Documentation-3.4|Diffusion Tensor Estimation]] (Raul San Jose Estepar)
*[[Modules:PythonExtractBaseline-Documentation-3.4|Python Extract Baseline DWI Volume]] (Julien von Siebenthal)
*Filter
**[[Modules:JointRicianLMMSEImageFilter-Documentation-3.4|Joint Rician LMMSE Image Filter]] (Antonio Tristán Vega, Santiago Aja Fernandez)
**[[Modules:RicianLMMSEImageFilter-Documentation-3.4|Rician LMMSE Image Filter]] (Antonio Tristán Vega, Santiago Aja Fernandez, Marc Niethammer)
**[[Modules:UnbiasedNonLocalMeans-Documentation-3.4|Unbiased Non Local Means filter for DWI]] (Antonio Tristán Vega, Santiago Aja Fernandez)
*[[Modules:ShiftDWIValues-Documentation-3.4|Python Shift DWI Values]] (Julien von Siebenthal)
*[[Modules:RecenterScalar2DWI-Documentation-3.4|Python Recenter Scalar to DWI Volume]] (Julien von Siebenthal)

====DTI====
*[[Modules:ResampleDTIVolume-Documentation-3.4|Resample DTI Volume]] (Francois Budin)
*[[Modules:DTIDisplay-Documentation-3.4|Display]] (Alex Yarmakovich)
*[[Modules:DiffusionTensorScalarMeasurements-Documentation-3.4 | Diffusion Tensor Scalar Measurements]] (Raul San Jose Estepar)
*Analysis
**[[Modules:FiducialSeeding-Documentation-3.4|Fiducial Seeding]] (Alex Yarmakovich, Steve Pieper)
**[[Modules:ROISeeding-Documentation-3.4 | ROI Seeding]] (Raul San Jose Estepar)
**[[Modules:StochasticTractography-Documentation-3.4|Python Stochastic Tractography]] (Julien von Siebenthal)
===IGT===
*[[Modules:OpenIGTLinkIF-Documentation-3.4| OpenIGTLinkIF Module]] (Junichi Tokuda)
*[[Modules:NeuroNav-Documentation-3.4| NeuroNav Module]] (Haiying Liu)
*[[Modules:ProstateNav-Documentation-3.4| ProstateNav Module]] (Junichi Tokuda)
===Filtering===
*[[Modules:CheckerboardFilter-Documentation-3.4|Checkerboard Filter]] (Bill Lorensen)
*[[Modules:HistogramMatching-Documentation-3.4|Histogram Matching]] (Bill Lorensen)
*[[Modules:ImageLabelCombine-3.4|Image Label Combine]] (Alex Yarmarkovich)
*[[Modules:ResampleVolume-Documentation-3.4|Resample Volume]] (Bill Lorensen)
*[[Modules:ResampleVolume2-Documentation-3.4|Resample Volume2]] (Francois Budin)
*[[Modules:ThresholdImage-Documentation-3.4|Threshold Image]] (Nicole Aucoin)
*Arithmetic
**[[Modules:AddImages-Documentation-3.4|Add Images]] (Bill Lorensen)
**[[Modules:SubtractImages-Documentation-3.4|Subtract Images]] (Bill Lorensen)
*Denoising
**[[Modules:GradientAnisotropicFilter-Documentation-3.4| Gradient Anisotropic Filter]] (Bill Lorensen checked this in)
**[[Modules:CurvatureAnisotropicDiffusion-Documentation-3.4|Curvature Anisotropic Diffusion]] (Bill Lorensen)
**[[Modules:GaussianBlur-Documentation-3.4|Gaussian Blur]] (Julien Jomier, Stephen Aylward)
**[[Modules:MedianFilter-Documentation-3.4|Median Filter]] (Bill Lorensen)
*Morphology
**[[Modules:VotingBinaryHoleFilling-Documentation-3.4|Voting Binary Hole Filling]] (Bill Lorensen)
**[[Modules:GrayscaleFillHole-Documentation-3.4|Grayscale Fill Hole]] (Bill Lorensen)
**[[Modules:GrayscaleGrindPeak-Documentation-3.4|Grayscale Grind Peak]] (Bill Lorensen)
===Surface Models===
*[[Modules:Model_Maker-Documentation-3.4| Modelmaker]] (Nicole Aucoin)
*Grayscale Model Maker (Bill Lorensen)

*Freesurfer Surface Section Extraction (Katharina Quintus)
*[[Modules:PythonSurfaceConnectivity-Documentation-3.4| Python Surface Connectivity]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonSurfaceICPRegistration-Documentation-3.4| Python Surface ICP Registration]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonSurfaceToolbox-Documentation-3.4| Python Surface Toolbox]] (Luca Antiga, Daniel Blezek)
*[[Modules:ClipModel-Documentation-3.4| Clip Model]] (Alex Yarmarkovich)
*[[Slicer3:Model_Into_Label_Volume_Documentation-3.4| Model into Label Volume]] (Nicole Aucoin)
===Python Modules===
*[[Modules:PythonExplodeVolumeTransform-Documentation-3.4| Python Explode Volume Transform]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonScript-Documentation-3.4| Python Script]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonNumpyScript-Documentation-3.4| Python Numpy Script]] (Luca Antiga, Daniel Blezek)

===Batch processing===
*[[Modules:EMSegment-Documentation-3.4|EM Segementer batch]] (Julien Jomier, Stephen Aylward)
*[[Modules:GaussianBlur-Documentation-3.4|Gaussian blur batch]] (Julien Jomier, Stephen Aylward)
*[[Modules:Registration-Documentation-3.4|Registration batch]] (Julien Jomier, Stephen Aylward)

===Converters===
*[[Modules:CreateaDicomSeries-Documentation-3.4|Create a Dicom Series]] (Bill Lorensen)
*[[Modules:DicomToNRRD-3.4|Dicom to NRRD]] (Xiaodong Tao)
*[[Modules:OrientImages-Documentation-3.4|Orient Images]] (Bill Lorensen)

===Demonstration===
*[[Modules:ExecutionModelTour-Documentation-3.4|Execution Model Tour]] (Daniel Blezek, Bill Lorensen)
*[[Modules:ScriptedModuleExample-Documentation-3.4| Scripted Module Example]] (Steve Pieper)

=Documented Modules=
(please do not touch this section for now. Ron)
==Main GUI==

==Modules==

===Core and Loadable Modules===

==CLI Modules==

=Modules for Downloading=
[[Image:SlicerOnNITRC2009.png|thumb|right|Slicer on NITRC]]
We are using NITRC as a repository for contributed modules. As a general rule, we do not test them ourselves, it is the downloaders job to ensure that they do what they want them to do.

Click [http://www.nitrc.org/search/?type_of_search=soft&words=slicer3&Search.x=0&Search.y=0&Search=Search here] to see a listing of Slicer 3 modules on NITRC.

Work is in progress to create infrastructure for searching an loading modules. See [[Slicer3:Loadable_Modules:Status_2009-01-20|here]] for more information.

Documentation/3.4

2009-03-03T10:13:21Z

Atriveg: /* DWI */

Note: This page is currently under construction

=Introduction=
[[Image:Base-Features-and-Modules.png|thumb|right|overview|[[Media:Integrating with Slicer3.ppt | Integrating with Slicer3]]]]
This page is a portal for documentation about Slicer 3.4.
For information for software developers, please go to the Developers page (see link in navigation box to the left).

=How-To Tutorials=
[http://wiki.na-mic.org/Wiki/index.php/Slicer3:Training Slicer3 tutorial page]

=Feature Request and Problem Reports=
We have an [http://www.na-mic.org/Bug/my_view_page.php issues tracker] for Slicer 3. You need to create an account for filing reports. We keep track of both feature requests and bug reports. Make sure to use the pull-down in the upper right to select Slicer 3.

=List of Modules in need of documentation=
== Requirements for modules to be added to the release==
{| border="00" cellpadding="5" cellspacing="0"
|-
| rowspan="2"|
* The module is '''feature complete''' for the tasks advertised on 2-4-2009
* The module has a '''test'''. See [http://wiki.na-mic.org/Wiki/index.php/Slicer3:Execution_Model_Testing '''here'''] for more information.
* Module has '''documentation''' on the [[Documentation-3.4#Modules|Slicer wiki]]. Please use the template provided [[Documentation-3.4#Modules|'''here''']] to structure your page.
*Please add a pointer to the documentation on the Slicer wiki to the the '''Help''' tab of the module. See the '''Editor module''' in Slicer for an example.
* The contributor (and their manager/advisor), the lab (with labs/institution logo) and the funding source (with grant number, logo optional) are listed in the '''Acknowledgment''' tab of the module. Please see the '''Models module''' for and example. The people listed in the acknowledgement will be the primary people for support and maintenance relative of the module.
** '''Style Guide:''' All acknowledgment icons should be 100x100 pixels, preferably in png format.
** '''Accessing logos:''' Icons for BIRN, NAC, NA-MIC and IGT are included in Slicer3/Base/GUI//vtkSlicerBaseAcknowledgementLogoIcons.cxx/h and resources for them are in Slicer3/Base/GUI/Resources/vtkSlicerBaseAcknowledgementLogos_ImageData.h. The API for vtkSlicerModuleGUI provides access to these icons.
** '''Adding logos:''' Please add additional image resources and logo icons to these files as required in order to promote shared use (and to prevent duplication in the code.)
* If your module has [[Documentation-3.2|documentation in Slicer 3.2]], please copy/paste/update into the 3.4 version
| style="background: #e5e5e5" align="center"| Examples for the Help and
Acknowledgment Panels
|-
| style="background: #ebeced"|[[Image:SlicerHelpExample.png|center|200px]][[Image:SlicerAcknowledgementExample.png|center|200px]]
|}

==Main GUI==
*[[Modules:MainApplicationGUI-Documentation-3.4| Main Application GUI]] (Wendy Plesniak)
*[[Modules:EventBindings-3.4| List of Hotkeys and Keyboard Shortcuts]] (Wendy Plesniak)
*[[Modules:Loading-Data-3.4| How to load data]] (Steve Pieper)
*[[Modules:Saving-Documentation-3.4| Save Scene and Data Module]] (Wendy Plesniak)

==Modules==
*Please copy the template linked below, paste it into your page and customize it with your module's information.
[[Slicer3:Module_Documentation-3.4_Template|Slicer3:Module_Documentation-3.4_Template]]
*See above for info to be put into the Help and Acknowledgment Tabs
*To put your lab's logo into a module, see [[Slicer3:Execution_Model_Documentation#Adding_Module_Logos_to_Slicer3|here]]
===Core and Loadable Modules===
*[[Modules:Welcome-Documentation-3.4| Welcome Module]] (Wendy Plesniak, Steve Pieper, Sonia Pujol, Ron Kikinis)

*[[Modules:Volumes-Documentation-3.4| Volumes Module]] (Alex Yarmarkovich, Steve Pieper)
**[[Modules:Volumes:Diffusion Editor-Documentation-3.4| Diffusion Editor]] (Kerstin Kessel)
*[[Modules:Models-Documentation-3.4| Models Module]] (Alex Yarmarkovich)

*[[Modules:Fiducials-Documentation-3.4| Fiducials Module]] (Nicole Aucoin)
*[[Modules:Data-Documentation-3.4| Data Module]] (Alex Yarmarkovich)
*[[Modules:Slices-Documentation-3.4|Slices Module]] (Jim Miller)
*[[Modules:Transforms-Documentation-3.4| Transforms Module]] (Alex Yarmarkovich)
*[[Modules:Color-Documentation-3.4| Color Module]] (Nicole Aucoin)

*[[Modules:Editor-Documentation-3.4| Interactive Editor]] (Steve Pieper)
*[[Modules:ROIModule-Documentation-3.4|ROI Module]] (Alex Yarmarkovich)
*[[Modules:VolumeRendering-Documentation-3.4| Volume Rendering Module]] (Alex Yarmarkovich)

==Other Modules==

Please adhere to the naming scheme for the module documentation:
*[ [Modules:MyModuleNameNoSpaces-Documentation-3.4|My Module Name With Spaces] ] (First Last Name)
===Wizards===
*[[Modules:ChangeTracker-Documentation-3.4|ChangeTracker]] (Andriy Fedorov)
*[[Modules:IA_FEMesh-Documentation-3.4|IA FE Meshing Module]] (Vince Magnotta)
===Informatics Modules===
*[[Modules:FetchMI-Documentation-3.4| Fetch Medical Informatics Module]] (Wendy Plesniak)
*[[Modules:QDECModule-Documentation-3.4| QDEC Module]] (Nicole Aucoin)
*[[Modules:QueryAtlas-Documentation-3.4|Query Atlas Module]] (Wendy Plesniak)
===Registration===
*[[Modules:AffineRegistration-Documentation-3.4|Affine Registration]] (Daniel Blezek)
*[[Modules:DeformableB-SplineRegistration-Documentation-3.4|Deformable B-Spline Registration]] (Bill Lorensen)
*[[Modules:DiffeomorphicDemonsAlgorithm-Documentation3.4|Diffeomorphic Demons Algorithm]] (Tom Vercauteren, Ender Konukoglu, Kilian Pohl)
*[[Modules:LinearRegistration-Documentation-3.4|Linear Registration]] (Daniel Blezek)
*[[Modules:RealignVolume-Documentation-3.4|Realign Volume]] (Nicole Aucoin)
*[[Modules:RigidRegistration-Documentation-3.4|Rigid Registration]] (Daniel Blezek)
===Segmentation===
*EM Segment Command-Line (Brad Davis, Will Schroeder)
*EM Segment Simple (Brad Davis, Will Schroeder)
*EMSegmentTemplateBuilder (Brad Davis, Will Schroeder)
*[[Modules:Simple Region Growing-Documentation-3.4|Simple Region Growing]] (Jim Miller)
*[[Modules:OtsuThreshold-Documentation-3.4|Otsu Threshold]] (Bill Lorensen)
===Statistics===
*Calculate Volume Statistics (Tri Ngo)
*[[Modules:LabelStatistics-Documentation-3.4|Label Statistics]] (Steve Pieper)
===DWI and Tractography===
====DWI====
*Estimation
**[[Modules:DiffusionTensorEstimation-Documentation-3.4|Diffusion Tensor Estimation]] (Raul San Jose Estepar)
*[[Modules:PythonExtractBaseline-Documentation-3.4|Python Extract Baseline DWI Volume]] (Julien von Siebenthal)
*Filter
**[[Modules:JointRicianLMMSEImageFilter-Documentation-3.4|Joint Rician LMMSE Image Filter]] (Antonio Trsitán Vega, Santiago Aja Fernandez)
**[[Modules:RicianLMMSEImageFilter-Documentation-3.4|Rician LMMSE Image Filter]] (Antonio Tristán Vega, Santiago Aja Fernandez, Marc Niethammer)
**[[Modules:UnbiasedNonLocalMeans-Documentation-3.4|Unbiased Non Local Means filter for DWI]] (Antonio Tristán Vega, Santiago Aja Fernandez)
*[[Modules:ShiftDWIValues-Documentation-3.4|Python Shift DWI Values]] (Julien von Siebenthal)
*[[Modules:RecenterScalar2DWI-Documentation-3.4|Python Recenter Scalar to DWI Volume]] (Julien von Siebenthal)

====DTI====
*[[Modules:ResampleDTIVolume-Documentation-3.4|Resample DTI Volume]] (Francois Budin)
*[[Modules:DTIDisplay-Documentation-3.4|Display]] (Alex Yarmakovich)
*[[Modules:DiffusionTensorScalarMeasurements-Documentation-3.4 | Diffusion Tensor Scalar Measurements]] (Raul San Jose Estepar)
*Analysis
**[[Modules:FiducialSeeding-Documentation-3.4|Fiducial Seeding]] (Alex Yarmakovich, Steve Pieper)
**[[Modules:ROISeeding-Documentation-3.4 | ROI Seeding]] (Raul San Jose Estepar)
**[[Modules:StochasticTractography-Documentation-3.4|Python Stochastic Tractography]] (Julien von Siebenthal)
===IGT===
*[[Modules:OpenIGTLinkIF-Documentation-3.4| OpenIGTLinkIF Module]] (Junichi Tokuda)
*[[Modules:NeuroNav-Documentation-3.4| NeuroNav Module]] (Haiying Liu)
*[[Modules:ProstateNav-Documentation-3.4| ProstateNav Module]] (Junichi Tokuda)
===Filtering===
*[[Modules:CheckerboardFilter-Documentation-3.4|Checkerboard Filter]] (Bill Lorensen)
*[[Modules:HistogramMatching-Documentation-3.4|Histogram Matching]] (Bill Lorensen)
*[[Modules:ImageLabelCombine-3.4|Image Label Combine]] (Alex Yarmarkovich)
*[[Modules:ResampleVolume-Documentation-3.4|Resample Volume]] (Bill Lorensen)
*[[Modules:ResampleVolume2-Documentation-3.4|Resample Volume2]] (Francois Budin)
*[[Modules:ThresholdImage-Documentation-3.4|Threshold Image]] (Nicole Aucoin)
*Arithmetic
**[[Modules:AddImages-Documentation-3.4|Add Images]] (Bill Lorensen)
**[[Modules:SubtractImages-Documentation-3.4|Subtract Images]] (Bill Lorensen)
*Denoising
**[[Modules:GradientAnisotropicFilter-Documentation-3.4| Gradient Anisotropic Filter]] (Bill Lorensen checked this in)
**[[Modules:CurvatureAnisotropicDiffusion-Documentation-3.4|Curvature Anisotropic Diffusion]] (Bill Lorensen)
**[[Modules:GaussianBlur-Documentation-3.4|Gaussian Blur]] (Julien Jomier, Stephen Aylward)
**[[Modules:MedianFilter-Documentation-3.4|Median Filter]] (Bill Lorensen)
*Morphology
**[[Modules:VotingBinaryHoleFilling-Documentation-3.4|Voting Binary Hole Filling]] (Bill Lorensen)
**[[Modules:GrayscaleFillHole-Documentation-3.4|Grayscale Fill Hole]] (Bill Lorensen)
**[[Modules:GrayscaleGrindPeak-Documentation-3.4|Grayscale Grind Peak]] (Bill Lorensen)
===Surface Models===
*[[Modules:Model_Maker-Documentation-3.4| Modelmaker]] (Nicole Aucoin)
*Grayscale Model Maker (Bill Lorensen)

*Freesurfer Surface Section Extraction (Katharina Quintus)
*[[Modules:PythonSurfaceConnectivity-Documentation-3.4| Python Surface Connectivity]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonSurfaceICPRegistration-Documentation-3.4| Python Surface ICP Registration]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonSurfaceToolbox-Documentation-3.4| Python Surface Toolbox]] (Luca Antiga, Daniel Blezek)
*[[Modules:ClipModel-Documentation-3.4| Clip Model]] (Alex Yarmarkovich)
*[[Slicer3:Model_Into_Label_Volume_Documentation-3.4| Model into Label Volume]] (Nicole Aucoin)
===Python Modules===
*[[Modules:PythonExplodeVolumeTransform-Documentation-3.4| Python Explode Volume Transform]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonScript-Documentation-3.4| Python Script]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonNumpyScript-Documentation-3.4| Python Numpy Script]] (Luca Antiga, Daniel Blezek)

===Batch processing===
*[[Modules:EMSegment-Documentation-3.4|EM Segementer batch]] (Julien Jomier, Stephen Aylward)
*[[Modules:GaussianBlur-Documentation-3.4|Gaussian blur batch]] (Julien Jomier, Stephen Aylward)
*[[Modules:Registration-Documentation-3.4|Registration batch]] (Julien Jomier, Stephen Aylward)

===Converters===
*[[Modules:CreateaDicomSeries-Documentation-3.4|Create a Dicom Series]] (Bill Lorensen)
*[[Modules:DicomToNRRD-3.4|Dicom to NRRD]] (Xiaodong Tao)
*[[Modules:OrientImages-Documentation-3.4|Orient Images]] (Bill Lorensen)

===Demonstration===
*[[Modules:ExecutionModelTour-Documentation-3.4|Execution Model Tour]] (Daniel Blezek, Bill Lorensen)
*[[Modules:ScriptedModuleExample-Documentation-3.4| Scripted Module Example]] (Steve Pieper)

=Documented Modules=
(please do not touch this section for now. Ron)
==Main GUI==

==Modules==

===Core and Loadable Modules===

==CLI Modules==

=Modules for Downloading=
[[Image:SlicerOnNITRC2009.png|thumb|right|Slicer on NITRC]]
We are using NITRC as a repository for contributed modules. As a general rule, we do not test them ourselves, it is the downloaders job to ensure that they do what they want them to do.

Click [http://www.nitrc.org/search/?type_of_search=soft&words=slicer3&Search.x=0&Search.y=0&Search=Search here] to see a listing of Slicer 3 modules on NITRC.

Work is in progress to create infrastructure for searching an loading modules. See [[Slicer3:Loadable_Modules:Status_2009-01-20|here]] for more information.

Modules:RicianLMMSEImageFilter-Documentation-3.4

2009-03-03T10:10:52Z

Atriveg: /* Module Description */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
RicianLMMSEImageFilter

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , Santiago Aja Fernández and Marc Niethammer
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The noise parameter is automatically estimated. New version using ITK pipelines and multi-threading.

* NOTE: This filter is different from jointLMMSE. In this case each DWI volume (data corresponding to each gradient direction) is filtered separately and independently, so in fact it works by performing N independent filtering operations. We strongly recommend to use jointLMMSE to filter DWI volumes, since it is more accurate and only slightly slower. Even if N=1 is set for jointLMMSE, its behavior is different (see the documentation for this other method).

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Minimum number of voxels for filtering:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, no filtering is performed.
* '''Minimum number of voxels for estimation:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, this pixel is not included in noise estimation
* '''Minimum noise std:''' If the estimated noise is too low, it is likely to occur that the estimation is non well done, so this minimum value is used instead.
* '''Maximum noise std:''' If the estimated noise is too high, it is likely to occur that the estimation is non well done, so this maximum value is used instead.
* '''Histogram resolution factor:''' Noise power is estimated as the mode of the histogram of local variances. This parameter fix how accurately this value is searched for. Too fine resolution may produce weak estimates of the histogram bins.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:RicianLMMSEImageFilter-Documentation-3.4

2009-03-03T10:10:14Z

Atriveg: /* Module Description */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
RicianLMMSEImageFilter

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , Santiago Aja Fernández and Marc Niethammer
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The noise parameter is automatically estimated. New version using ITK pipelines and multi-threading.

* NOTE: This filter is different from jointLMMSE. In this case each DWI volume (data corresponding to each gradient direction) is filtered separately and independently, so in fact it works by performing N independent filter operations. We strongly recommend to use jointLMMSE to filter DWI volumes, since it is more accurate and only slightly slower. Even if N=1 is set for jointLMMSE, its behavior is different (see the documentation for this other method).

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Minimum number of voxels for filtering:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, no filtering is performed.
* '''Minimum number of voxels for estimation:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, this pixel is not included in noise estimation
* '''Minimum noise std:''' If the estimated noise is too low, it is likely to occur that the estimation is non well done, so this minimum value is used instead.
* '''Maximum noise std:''' If the estimated noise is too high, it is likely to occur that the estimation is non well done, so this maximum value is used instead.
* '''Histogram resolution factor:''' Noise power is estimated as the mode of the histogram of local variances. This parameter fix how accurately this value is searched for. Too fine resolution may produce weak estimates of the histogram bins.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:RicianLMMSEImageFilter-Documentation-3.4

2009-03-03T10:09:52Z

Atriveg: /* Module Description */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
RicianLMMSEImageFilter

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , Santiago Aja Fernández and Marc Niethammer
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The noise parameter is automatically estimated. New version using ITK pipelines and multi-threading.

Note that this filter is different from jointLMMSE. In this case each DWI volume (data corresponding to each gradient direction) is filtered separately and independently, so in fact it works by performing N independent filter operations. We strongly recommend to use jointLMMSE to filter DWI volumes, since it is more accurate and only slightly slower. Even if N=1 is set for jointLMMSE, its behavior is different (see the documentation for this other method).

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Minimum number of voxels for filtering:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, no filtering is performed.
* '''Minimum number of voxels for estimation:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, this pixel is not included in noise estimation
* '''Minimum noise std:''' If the estimated noise is too low, it is likely to occur that the estimation is non well done, so this minimum value is used instead.
* '''Maximum noise std:''' If the estimated noise is too high, it is likely to occur that the estimation is non well done, so this maximum value is used instead.
* '''Histogram resolution factor:''' Noise power is estimated as the mode of the histogram of local variances. This parameter fix how accurately this value is searched for. Too fine resolution may produce weak estimates of the histogram bins.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:JointRicianLMMSEImageFilter-Documentation-3.4

2009-03-03T10:08:53Z

Atriveg: /* Quick Tour of Features and Use */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
jointLMMSE

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega and Santiago Aja Fernández
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results. The noise parameter is automatically estimated (noise estimation improved but slower). A complete description of the algorithm may be found in "DWI filtering using joint information for DTI and HARDI", by Antonio Tristan Vega and Santiago Aja-Fernandez (under review).

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.
** NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:RicianLMMSEImageFilter-Documentation-3.4

2009-03-03T10:06:24Z

Atriveg: /* Module Description */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
RicianLMMSEImageFilter

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , Santiago Aja Fernández and Marc Niethammer
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The noise parameter is automatically estimated. New version using ITK pipelines and multi-threading.

Note that this filter is different from jointLMMSE. In this case each DWI volume (data corresponding to each gradient direction) is filtered separately and independently, so in fact it works by performing N independent filter operations. We strongly recommend to use jointLMMSE to filter DWI volumes, since it is more accurate and only slightly slower.

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Minimum number of voxels for filtering:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, no filtering is performed.
* '''Minimum number of voxels for estimation:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, this pixel is not included in noise estimation
* '''Minimum noise std:''' If the estimated noise is too low, it is likely to occur that the estimation is non well done, so this minimum value is used instead.
* '''Maximum noise std:''' If the estimated noise is too high, it is likely to occur that the estimation is non well done, so this maximum value is used instead.
* '''Histogram resolution factor:''' Noise power is estimated as the mode of the histogram of local variances. This parameter fix how accurately this value is searched for. Too fine resolution may produce weak estimates of the histogram bins.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:RicianLMMSEImageFilter-Documentation-3.4

2009-03-03T10:03:23Z

Atriveg: /* Quick Tour of Features and Use */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
RicianLMMSEImageFilter

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , Santiago Aja Fernández and Marc Niethammer
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The noise parameter is automatically estimated. New version using ITK pipelines and multi-threading.

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Minimum number of voxels for filtering:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, no filtering is performed.
* '''Minimum number of voxels for estimation:''' The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, this pixel is not included in noise estimation
* '''Minimum noise std:''' If the estimated noise is too low, it is likely to occur that the estimation is non well done, so this minimum value is used instead.
* '''Maximum noise std:''' If the estimated noise is too high, it is likely to occur that the estimation is non well done, so this maximum value is used instead.
* '''Histogram resolution factor:''' Noise power is estimated as the mode of the histogram of local variances. This parameter fix how accurately this value is searched for. Too fine resolution may produce weak estimates of the histogram bins.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:UnbiasedNonLocalMeans-Documentation-3.4

2009-03-03T09:57:50Z

Atriveg: /* Quick Tour of Features and Use */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
UnbiasedNonLocalMeans

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega , Santiago Aja Fernández
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images using Unbiased Non Local Means for Rician noise. It exploits not only the spatial redundancy, but the redundancy in similar gradient directions as well; it takes into account the N closest gradient directions to the direction being processed (a maximum of 5 gradient directions is allowed to keep a reasonable computational load, since we do not use neither similarity maps nor block-wise implementation). The noise parameter is automatically estimated. A complete description of the algorithm may be found in "DWI filtering using joint information for DTI and HARDI", by Antonio Tristan Vega and Santiago Aja-Fernandez (under review).

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Search radius:''' This filter works by computing the weighted average of pixels in a large neighborhood. The search radius is the 3D radius of that neighborhood.
* '''Comparison radius:''' The weights of the average are computed as the negative exponential of the (normalized) distance between two neighborhoods which have a 3D radius of this size.
* '''h:''' The normalization constant for the distance between neighborhoods. This parameter is related to the noise power, and should be in the range 0.8->1.2. Higher values produce more blurring (and more noise reduction), while lower values better preserve edges.
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. It is strongly recommended to keep this parameter less than 5 to keep a reasonable computational load. Even so, this filter is extremely slow.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

This filter is painfully slow. May take hours.

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:JointRicianLMMSEImageFilter-Documentation-3.4

2009-03-03T09:26:37Z

Atriveg: /* Usage */

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
jointLMMSE

== General Information ==
===Module Type & Category===

Type: Interactive

Category: CLI/DiffusionApplications

===Authors, Collaborators & Contact===
* Author: Antonio Tristán Vega and Santiago Aja Fernández
* Contact: atriveg@bwh.harvard.edu

===Module Description===
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results. The noise parameter is automatically estimated (noise estimation improved but slower). A complete description of the algorithm may be found in "DWI filtering using joint information for DTI and HARDI", by Antonio Tristan Vega and Santiago Aja-Fernandez (under review).

== Usage ==

===Examples, Use Cases & Tutorials===

===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
* '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.

== Development ==

===Dependencies===

Volumes. Needed to load DWI volumes

===Known bugs===

===Usability issues===

===Source code & documentation===

== More Information ==

===Acknowledgment===
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

===References===

Modules:UnbiasedNonLocalMeans-Documentation-3.4

2009-03-02T17:08:30Z

Atriveg: New page: Return to Slicer 3.4 Documentation __NOTOC__ ===Module Name=== UnbiasedNonLocalMeans == General Information == ===Module Type & Category=== Type: Interactive Cat...

Modules:RicianLMMSEImageFilter-Documentation-3.4

2009-03-02T17:05:50Z

Atriveg: New page: Return to Slicer 3.4 Documentation __NOTOC__ ===Module Name=== RicianLMMSEImageFilter == General Information == ===Module Type & Category=== Type: Interactive Ca...

Modules:JointRicianLMMSEImageFilter-Documentation-3.4

2009-03-02T17:02:21Z

Atriveg: New page: Return to Slicer 3.4 Documentation __NOTOC__ ===Module Name=== jointLMMSE == General Information == ===Module Type & Category=== Type: Interactive Category: CLI/...

Documentation/3.4

2009-03-02T16:58:09Z

Atriveg:

Note: This page is currently under construction

=Introduction=
[[Image:Base-Features-and-Modules.png|thumb|right|overview|[[Media:Integrating with Slicer3.ppt | Integrating with Slicer3]]]]
This page is a portal for documentation about Slicer 3.4.
For information for software developers, please go to the Developers page (see link in navigation box to the left).

=How-To Tutorials=
[http://wiki.na-mic.org/Wiki/index.php/Slicer3:Training Slicer3 tutorial page]

=Feature Request and Problem Reports=
We have an [http://www.na-mic.org/Bug/my_view_page.php issues tracker] for Slicer 3. You need to create an account for filing reports. We keep track of both feature requests and bug reports. Make sure to use the pull-down in the upper right to select Slicer 3.

=List of Modules in need of documentation=
== Requirements for modules to be added to the release==
{| border="00" cellpadding="5" cellspacing="0"
|-
| rowspan="2"|
* The module is '''feature complete''' for the tasks advertised on 2-4-2009
* The module has a '''test'''. See [http://wiki.na-mic.org/Wiki/index.php/Slicer3:Execution_Model_Testing '''here'''] for more information.
* Module has '''documentation''' on the [[Documentation-3.4#Modules|Slicer wiki]]. Please use the template provided [[Documentation-3.4#Modules|'''here''']] to structure your page.
*Please add a pointer to the documentation on the Slicer wiki to the the '''Help''' tab of the module. See the '''Editor module''' in Slicer for an example.
* The contributor (and their manager/advisor), the lab (with labs/institution logo) and the funding source (with grant number, logo optional) are listed in the '''Acknowledgment''' tab of the module. Please see the '''Models module''' for and example. The people listed in the acknowledgement will be the primary people for support and maintenance relative of the module.
** '''Style Guide:''' All acknowledgment icons should be 100x100 pixels, preferably in png format.
** '''Accessing logos:''' Icons for BIRN, NAC, NA-MIC and IGT are included in Slicer3/Base/GUI//vtkSlicerBaseAcknowledgementLogoIcons.cxx/h and resources for them are in Slicer3/Base/GUI/Resources/vtkSlicerBaseAcknowledgementLogos_ImageData.h. The API for vtkSlicerModuleGUI provides access to these icons.
** '''Adding logos:''' Please add additional image resources and logo icons to these files as required in order to promote shared use (and to prevent duplication in the code.)
* If your module has [[Documentation-3.2|documentation in Slicer 3.2]], please copy/paste/update into the 3.4 version
| style="background: #e5e5e5" align="center"| Examples for the Help and
Acknowledgment Panels
|-
| style="background: #ebeced"|[[Image:SlicerHelpExample.png|center|200px]][[Image:SlicerAcknowledgementExample.png|center|200px]]
|}

==Main GUI==
*[[Modules:MainApplicationGUI-Documentation-3.4| Main Application GUI]] (Wendy Plesniak)
*[[Modules:EventBindings-3.4| List of Hotkeys and Keyboard Shortcuts]] (Wendy Plesniak)
*[[Modules:Loading-Data-3.4| How to load data]] (Steve Pieper)
*[[Modules:Saving-Documentation-3.4| Save Scene and Data Module]] (Wendy Plesniak)

==Modules==
*Please copy the template linked below, paste it into your page and customize it with your module's information.
[[Slicer3:Module_Documentation-3.4_Template|Slicer3:Module_Documentation-3.4_Template]]
*See above for info to be put into the Help and Acknowledgment Tabs
*To put your lab's logo into a module, see [[Slicer3:Execution_Model_Documentation#Adding_Module_Logos_to_Slicer3|here]]
===Core and Loadable Modules===
*[[Modules:Welcome-Documentation-3.4| Welcome Module]] (Wendy Plesniak, Steve Pieper, Sonia Pujol, Ron Kikinis)

*[[Modules:Volumes-Documentation-3.4| Volumes Module]] (Alex Yarmarkovich, Steve Pieper)
**[[Modules:Volumes:Diffusion Editor-Documentation-3.4| Diffusion Editor]] (Kerstin Kessel)
*[[Modules:Models-Documentation-3.4| Models Module]] (Alex Yarmarkovich)

*[[Modules:Fiducials-Documentation-3.4| Fiducials Module]] (Nicole Aucoin)
*[[Modules:Data-Documentation-3.4| Data Module]] (Alex Yarmarkovich)
*[[Modules:Slices-Documentation-3.4|Slices Module]] (Jim Miller)
*[[Modules:Transforms-Documentation-3.4| Transforms Module]] (Alex Yarmarkovich)
*[[Modules:Color-Documentation-3.4| Color Module]] (Nicole Aucoin)

*[[Modules:Editor-Documentation-3.4| Interactive Editor]] (Steve Pieper)
*[[Modules:ROIModule-Documentation-3.4|ROI Module]] (Alex Yarmarkovich)
*[[Modules:VolumeRendering-Documentation-3.4| Volume Rendering Module]] (Alex Yarmarkovich)

==Other Modules==

Please adhere to the naming scheme for the module documentation:
*[ [Modules:MyModuleNameNoSpaces-Documentation-3.4|My Module Name With Spaces] ] (First Last Name)
===Wizards===
*[[Modules:ChangeTracker-Documentation-3.4|ChangeTracker]] (Andriy Fedorov)
*[[Modules:IA_FEMesh-Documentation-3.4|IA FE Meshing Module]] (Vince Magnotta)
===Informatics Modules===
*[[Modules:FetchMI-Documentation-3.4| Fetch Medical Informatics Module]] (Wendy Plesniak)
*[[Modules:QDECModule-Documentation-3.4| QDEC Module]] (Nicole Aucoin)
*[[Modules:QueryAtlas-Documentation-3.4|Query Atlas Module]] (Wendy Plesniak)
===IGT===
*[[Modules:OpenIGTLinkIF-Documentation-3.4| OpenIGTLinkIF Module]] (Junichi Tokuda)
*[[Modules:NeuroNav-Documentation-3.4| NeuroNav Module]] (Haiying Liu)
*[[Modules:ProstateNav-Documentation-3.4| ProstateNav Module]] (Junichi Tokuda)

===Batch processing===
*[[Modules:EMSegment-Documentation-3.4|EM Segementer batch]] (Julien Jomier, Stephen Aylward)
*[[Modules:GaussianBlur-Documentation-3.4|Gaussian blur batch]] (Julien Jomier, Stephen Aylward)
*[[Modules:Registration-Documentation-3.4|Registration batch]] (Julien Jomier, Stephen Aylward)

===Converters===
*[[Modules:CreateaDicomSeries-Documentation-3.4|Create a Dicom Series]] (Bill Lorensen)
*[[Modules:DicomToNRRD-3.4|Dicom to NRRD]] (Xiaodong Tao)
*[[Modules:OrientImages-Documentation-3.4|Orient Images]] (Bill Lorensen)

===Demonstration===
*[[Modules:ExecutionModelTour-Documentation-3.4|Execution Model Tour]] (Daniel Blezek, Bill Lorensen)
*[[Modules:ScriptedModuleExample-Documentation-3.4| Scripted Module Example]] (Steve Pieper)

===Filtering===
*[[Modules:CheckerboardFilter-Documentation-3.4|Checkerboard Filter]] (Bill Lorensen)
*[[Modules:HistogramMatching-Documentation-3.4|Histogram Matching]] (Bill Lorensen)
*[[Modules:ImageLabelCombine-3.4|Image Label Combine]] (Alex Yarmarkovich)
*[[Modules:ResampleVolume-Documentation-3.4|Resample Volume]] (Bill Lorensen)
*[[Modules:ResampleVolume2-Documentation-3.4|Resample Volume2]] (Francois Budin)
*[[Modules:ThresholdImage-Documentation-3.4|Threshold Image]] (Nicole Aucoin)
*Arithmetic
**[[Modules:AddImages-Documentation-3.4|Add Images]] (Bill Lorensen)
**[[Modules:SubtractImages-Documentation-3.4|Subtract Images]] (Bill Lorensen)
*Denoising
**[[Modules:GradientAnisotropicFilter-Documentation-3.4| Gradient Anisotropic Filter]] (Bill Lorensen checked this in)
**[[Modules:CurvatureAnisotropicDiffusion-Documentation-3.4|Curvature Anisotropic Diffusion]] (Bill Lorensen)
**[[Modules:GaussianBlur-Documentation-3.4|Gaussian Blur]] (Julien Jomier, Stephen Aylward)
**[[Modules:MedianFilter-Documentation-3.4|Median Filter]] (Bill Lorensen)
*Morphology
**[[Modules:VotingBinaryHoleFilling-Documentation-3.4|Voting Binary Hole Filling]] (Bill Lorensen)
**[[Modules:GrayscaleFillHole-Documentation-3.4|Grayscale Fill Hole]] (Bill Lorensen)
**[[Modules:GrayscaleGrindPeak-Documentation-3.4|Grayscale Grind Peak]] (Bill Lorensen)

===Surface Models===
*[[Modules:Model_Maker-Documentation-3.4| Modelmaker]] (Nicole Aucoin)
*Grayscale Model Maker (Bill Lorensen)

*Freesurfer Surface Section Extraction (Katharina Quintus)
*[[Modules:PythonSurfaceConnectivity-Documentation-3.4| Python Surface Connectivity]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonSurfaceICPRegistration-Documentation-3.4| Python Surface ICP Registration]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonSurfaceToolbox-Documentation-3.4| Python Surface Toolbox]] (Luca Antiga, Daniel Blezek)
*[[Modules:ClipModel-Documentation-3.4| Clip Model]] (Alex Yarmarkovich)
*[[Slicer3:Model_Into_Label_Volume_Documentation-3.4| Model into Label Volume]] (Nicole Aucoin)

===Python Modules===
*[[Modules:PythonExplodeVolumeTransform-Documentation-3.4| Python Explode Volume Transform]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonScript-Documentation-3.4| Python Script]] (Luca Antiga, Daniel Blezek)
*[[Modules:PythonNumpyScript-Documentation-3.4| Python Numpy Script]] (Luca Antiga, Daniel Blezek)

===Registration===
*[[Modules:AffineRegistration-Documentation-3.4|Affine Registration]] (Daniel Blezek)
*[[Modules:DeformableB-SplineRegistration-Documentation-3.4|Deformable B-Spline Registration]] (Bill Lorensen)
*[[Modules:DiffeomorphicDemonsAlgorithm-Documentation3.4|Diffeomorphic Demons Algorithm]] (Tom Vercauteren, Ender Konukoglu, Kilian Pohl)
*[[Modules:LinearRegistration-Documentation-3.4|Linear Registration]] (Daniel Blezek)
*[[Modules:RealignVolume-Documentation-3.4|Realign Volume]] (Nicole Aucoin)
*[[Modules:RigidRegistration-Documentation-3.4|Rigid Registration]] (Daniel Blezek)

===Segmentation===
*EM Segment Command-Line (Brad Davis, Will Schroeder)
*EM Segment Simple (Brad Davis, Will Schroeder)
*EMSegmentTemplateBuilder (Brad Davis, Will Schroeder)
*[[Modules:Simple Region Growing-Documentation-3.4|Simple Region Growing]] (Jim Miller)
*[[Modules:OtsuThreshold-Documentation-3.4|Otsu Threshold]] (Bill Lorensen)

===Statistics===
*Calculate Volume Statistics (Tri Ngo)
*[[Modules:LabelStatistics-Documentation-3.4|Label Statistics]] (Steve Pieper)

===DWI and Tractography===
====DWI====
*Estimation
**[[Modules:DiffusionTensorEstimation-Documentation-3.4|Diffusion Tensor Estimation]] (Raul San Jose Estepar)
*[[Modules:PythonExtractBaseline-Documentation-3.4|Python Extract Baseline DWI Volume]] (Julien von Siebenthal)
*Filter
**[[Modules:JointRicianLMMSEImageFilter-Documentation-3.4|Joint Rician LMMSE Image Filter]] (Antonio Tritan Vega, Santiago Aja Fernandez)
**[[Modules:RicianLMMSEImageFilter-Documentation-3.4|Rician LMMSE Image Filter]] (Antonio Tritan Vega, Santiago Aja Fernandez)
**[[Modules:UnbiasedNonLocalMeans-Documentation-3.4|Unbiased Non Local Means filter for DWI]] (Antonio Tritan Vega, Santiago Aja Fernandez)
*[[Modules:ShiftDWIValues-Documentation-3.4|Python Shift DWI Values]] (Julien von Siebenthal)
*[[Modules:RecenterScalar2DWI-Documentation-3.4|Python Recenter Scalar to DWI Volume]] (Julien von Siebenthal)

====DTI====
*[[Modules:ResampleDTIVolume-Documentation-3.4|Resample DTI Volume]] (Francois Budin)
*[[Modules:DTIDisplay-Documentation-3.4|Display]] (Alex Yarmakovich)
*[[Modules:DiffusionTensorScalarMeasurements-Documentation-3.4 | Diffusion Tensor Scalar Measurements]] (Raul San Jose Estepar)
*Analysis
**[[Modules:FiducialSeeding-Documentation-3.4|Fiducial Seeding]] (Alex Yarmakovich, Steve Pieper)
**[[Modules:ROISeeding-Documentation-3.4 | ROI Seeding]] (Raul San Jose Estepar)
**[[Modules:StochasticTractography-Documentation-3.4|Python Stochastic Tractography]] (Julien von Siebenthal)

=Documented Modules=
(please do not touch this section for now. Ron)
==Main GUI==

==Modules==

===Core and Loadable Modules===

==CLI Modules==

=Modules for Downloading=
[[Image:SlicerOnNITRC2009.png|thumb|right|Slicer on NITRC]]
We are using NITRC as a repository for contributed modules. As a general rule, we do not test them ourselves, it is the downloaders job to ensure that they do what they want them to do.

Click [http://www.nitrc.org/search/?type_of_search=soft&words=slicer3&Search.x=0&Search.y=0&Search=Search here] to see a listing of Slicer 3 modules on NITRC.

Work is in progress to create infrastructure for searching an loading modules. See [[Slicer3:Loadable_Modules:Status_2009-01-20|here]] for more information.