Difference between revisions of "Modules:UnbiasedNonLocalMeans-Documentation-3.6"
|Line 61:||Line 61:|
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially by grant number TEC2007-67073/TCM from the de Ciencia (Spain) .
Revision as of 14:28, 13 May 2010Home < Modules:UnbiasedNonLocalMeans-Documentation-3.6
Module Type & Category
Authors, Collaborators & Contact
- Author: Antonio Tristán Vega , Santiago Aja Fernández
- Contact: email@example.com
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.
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.
Volumes. Needed to load DWI volumes
This filter is very slow. May take hours.
Source code & documentation
Source Code: Follow this link
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.