Difference between revisions of "Modules:UnbiasedNonLocalMeans-Documentation-3.4"
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* '''Input DWI Volume:''' set the DWI volume | * '''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 == | == Development == | ||
Latest revision as of 20:06, 26 May 2009
Home < Modules:UnbiasedNonLocalMeans-Documentation-3.4Return to Slicer 3.4 Documentation
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).
