interfaces.semtools.filtering.denoising

UnbiasedNonLocalMeans

Link to code

Wraps command ** UnbiasedNonLocalMeans **

title: Unbiased NLM for MRI

category: Filtering.Denoising

description: This module implements a fast version of the popular Non-Local Means filter for image denoising. This algorithm filters each pixel as a weighted average of its neighbors in a large vicinity. The weights are computed based on the similarity of each neighbor with the voxel to be denoised.

In the original formulation a patch with a certain radius is centered in each of the voxels, and the Mean Squared Error between each pair of corresponding voxels is computed. In this implementation, only the mean value and gradient components are compared. This, together with an efficient memory management, can attain a speed-up of nearly 20x. Besides, the filtering is more accurate than the original with poor SNR. This code is intended for its use with MRI (or any other Rician-distributed modality): the second order moment is estimated, then we subtract twice the squared power of noise, and finally we take the square root of the result to remove the Rician bias. The original implementation of the NLM filter may be found in: A. Buades, B. Coll, J. Morel, “A review of image denoising algorithms, with a new one”, Multiscale Modelling and Simulation 4(2): 490-530. 2005. The correction of the Rician bias is described in the following reference (among others): S. Aja-Fernandez, K. Krissian, “An unbiased Non-Local Means scheme for DWI filtering”, in: Proceedings of the MICCAI Workshop on Computational Diffusion MRI, 2008, pp. 277-284. The whole description of this version may be found in the following paper (please, cite it if you are willing to use this software): A. Tristan-Vega, V. Garcia Perez, S. Aja-Fenandez, and C.-F. Westin, “Efficient and Robust Nonlocal Means Denoising of MR Data Based on Salient Features Matching”, Computer Methods and Programs in Biomedicine. (Accepted for publication) 2011.

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

documentation-url: http://www.slicer.org/slicerWiki/index.php/Modules:UnbiasedNonLocalMeans-Documentation-3.6

contributor: Antonio Tristan Vega, Veronica Garcia-Perez, Santiago Aja-Fernandez, Carl-Fredrik Westin

acknowledgements: Supported by grant number FMECD-2010/71131616E from the Spanish Ministry of Education/Fulbright Committee

Inputs:

[Mandatory]

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
hp: (a float)
        This parameter is related to noise; the larger the parameter, the
        more aggressive 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 MRI volume.
        flag: %s, position: -2
outputVolume: (a boolean or a file name)
        Output (filtered) MRI volume.
        flag: %s, position: -1
ps: (a float)
        To accelerate computations, preselection is used: if the normalized
        difference is above this threshold, the voxel will be discarded (non
        used for average)
        flag: --ps %f
rc: (a list of items which are an integer (int or long))
        Similarity between blocks is computed as the difference between mean
        values and gradients. These parameters are computed fitting a
        hyperplane with LS inside a neighborhood of this size
        flag: --rc %s
rs: (a list of items which are an integer (int or long))
        The algorithm search for similar voxels in a neighborhood of this
        radius (radii larger than 5,5,5 are very slow, and the results can
        be only marginally better. Small radii may fail to effectively
        remove the noise).
        flag: --rs %s
sigma: (a float)
        The root power of noise (sigma) in the complex Gaussian process the
        Rician comes from. If it is underestimated, the algorithm fails to
        remove the noise. If it is overestimated, over-blurring is likely to
        occur.
        flag: --sigma %f
terminal_output: (u'stream' or u'allatonce' or u'file' or u'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 (filtered) MRI volume.