# 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 == |

## Revision as of 09:57, 3 March 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).