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# interfaces.slicer.legacy.diffusion.denoising

## DWIUnbiasedNonLocalMeansFilter

Link to code

Wraps command **DWIUnbiasedNonLocalMeansFilter **

title: DWI Unbiased Non Local Means Filter

category: Legacy.Diffusion.Denoising

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 Tristan-Vega and Santiago Aja-Fernandez, 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.

version: 0.0.1.$Revision: 1 $(alpha)

documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/UnbiasedNonLocalMeansFilterForDWI

contributor: Antonio Tristan Vega (UVa), Santiago Aja Fernandez (UVa)

acknowledgements: Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

Inputs:

    [Mandatory]

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    hp: (a float)
            This parameter is related to noise; the larger the parameter, the
            more agressive the filtering. Should be near 1, and only values
            between 0.8 and 1.2 are allowed
            flag: --hp %f
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    inputVolume: (an existing file name)
            Input DWI volume.
            flag: %s, position: -2
    ng: (an integer)
            The number of the closest gradients that are used to jointly filter
            a given gradient direction (a maximum of 5 is allowed).
            flag: --ng %d
    outputVolume: (a boolean or a file name)
            Output DWI volume.
            flag: %s, position: -1
    rc: (a list of items which are an integer)
            Similarity between blocks is measured using windows of this size.
            flag: --rc %s
    re: (a list of items which are an integer)
            A neighborhood of this size is used to compute the statistics for
            noise estimation.
            flag: --re %s
    rs: (a list of items which are an integer)
            The algorithm search for similar voxels in a neighborhood of this
            size (larger sizes than the default one are extremely slow).
            flag: --rs %s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

Outputs:

    outputVolume: (an existing file name)
            Output DWI volume.