.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.slicer.legacy.diffusion.denoising
============================================


.. _nipype.interfaces.slicer.legacy.diffusion.denoising.DWIUnbiasedNonLocalMeansFilter:


.. index:: DWIUnbiasedNonLocalMeansFilter

DWIUnbiasedNonLocalMeansFilter
------------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/legacy/diffusion/denoising.py#L23>`__

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]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [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: (an integer)
                Similarity between blocks is measured using windows of this size.
                flag: --rc %s
        re: (an integer)
                A neighborhood of this size is used to compute the statistics for
                noise estimation.
                flag: --re %s
        rs: (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

Outputs::

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