Home · Quickstart · Documentation · Citation · NiPy

# interfaces.slicer.filtering.denoising

## CurvatureAnisotropicDiffusion

Link to code

Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch CurvatureAnisotropicDiffusion **

title: Curvature Anisotropic Diffusion

category: Filtering.Denoising

description: Performs anisotropic diffusion on an image using a modified curvature diffusion equation (MCDE).

MCDE does not exhibit the edge enhancing properties of classic anisotropic diffusion, which can under certain conditions undergo a ‘negative’ diffusion, which enhances the contrast of edges. Equations of the form of MCDE always undergo positive diffusion, with the conductance term only varying the strength of that diffusion.

> Qualitatively, MCDE compares well with other non-linear diffusion techniques. It is less sensitive to contrast than classic Perona-Malik style diffusion, and preserves finer detailed structures in images. There is a potential speed trade-off for using this function in place of Gradient Anisotropic Diffusion. Each iteration of the solution takes roughly twice as long. Fewer iterations, however, may be required to reach an acceptable solution.

version: 0.1.0.$Revision: 19608 $(alpha)

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

contributor: Bill Lorensen (GE)

acknowledgements: This command module was derived from Insight/Examples (copyright) Insight Software Consortium

Inputs:

    [Mandatory]

    [Optional]
    args: (a string)
            Additional parameters to the command
    conductance: (a float)
            Conductance controls the sensitivity of the conductance term. As a general rule, the
            lower the value, the more strongly the filter preserves edges. A high value will cause
            diffusion (smoothing) across edges. Note that the number of iterations controls how much
            smoothing is done within regions bounded by edges.
    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
    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 volume to be filtered
    iterations: (an integer)
            The more iterations, the more smoothing. Each iteration takes the same amount of time.
            If it takes 10 seconds for one iteration, then it will take 100 seconds for 10
            iterations. Note that the conductance controls how much each iteration smooths across
            edges.
    outputVolume: (a boolean or a file name)
            Output filtered
    timeStep: (a float)
            The time step depends on the dimensionality of the image. In Slicer the images are 3D
            and the default (.0625) time step will provide a stable solution.

Outputs:

    outputVolume: (an existing file name)
            Output filtered

## GaussianBlurImageFilter

Link to code

Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch GaussianBlurImageFilter **

title: Gaussian Blur Image Filter

category: Filtering.Denoising

description: Apply a gaussian blurr to an image

version: 0.1.0.$Revision: 1.1 $(alpha)

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

contributor: Julien Jomier (Kitware), Stephen Aylward (Kitware)

acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.

Inputs:

    [Mandatory]

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    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 volume
    outputVolume: (a boolean or a file name)
            Blurred Volume
    sigma: (a float)
            Sigma value in physical units (e.g., mm) of the Gaussian kernel

Outputs:

    outputVolume: (an existing file name)
            Blurred Volume

## GradientAnisotropicDiffusion

Link to code

Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch GradientAnisotropicDiffusion **

title: Gradient Anisotropic Diffusion

category: Filtering.Denoising

description: Runs gradient anisotropic diffusion on a volume.

Anisotropic diffusion methods reduce noise (or unwanted detail) in images while preserving specific image features, like edges. For many applications, there is an assumption that light-dark transitions (edges) are interesting. Standard isotropic diffusion methods move and blur light-dark boundaries. Anisotropic diffusion methods are formulated to specifically preserve edges. The conductance term for this implementation is a function of the gradient magnitude of the image at each point, reducing the strength of diffusion at edges. The numerical implementation of this equation is similar to that described in the Perona-Malik paper, but uses a more robust technique for gradient magnitude estimation and has been generalized to N-dimensions.

version: 0.1.0.$Revision: 19608 $(alpha)

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

contributor: Bill Lorensen (GE)

acknowledgements: This command module was derived from Insight/Examples (copyright) Insight Software Consortium

Inputs:

    [Mandatory]

    [Optional]
    args: (a string)
            Additional parameters to the command
    conductance: (a float)
            Conductance controls the sensitivity of the conductance term. As a general rule, the
            lower the value, the more strongly the filter preserves edges. A high value will cause
            diffusion (smoothing) across edges. Note that the number of iterations controls how much
            smoothing is done within regions bounded by edges.
    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
    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 volume to be filtered
    iterations: (an integer)
            The more iterations, the more smoothing. Each iteration takes the same amount of time.
            If it takes 10 seconds for one iteration, then it will take 100 seconds for 10
            iterations. Note that the conductance controls how much each iteration smooths across
            edges.
    outputVolume: (a boolean or a file name)
            Output filtered
    timeStep: (a float)
            The time step depends on the dimensionality of the image. In Slicer the images are 3D
            and the default (.0625) time step will provide a stable solution.

Outputs:

    outputVolume: (an existing file name)
            Output filtered

## MedianImageFilter

Link to code

Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch MedianImageFilter **

title: Median Image Filter

category: Filtering.Denoising

description: The MedianImageFilter is commonly used as a robust approach for noise reduction. This filter is particularly efficient against “salt-and-pepper” noise. In other words, it is robust to the presence of gray-level outliers. MedianImageFilter computes the value of each output pixel as the statistical median of the neighborhood of values around the corresponding input pixel.

version: 0.1.0.$Revision: 19608 $(alpha)

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

contributor: Bill Lorensen (GE)

acknowledgements: This command module was derived from Insight/Examples/Filtering/MedianImageFilter (copyright) Insight Software Consortium

Inputs:

    [Mandatory]

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    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 volume to be filtered
    neighborhood: (an integer)
            The size of the neighborhood in each dimension
    outputVolume: (a boolean or a file name)
            Output filtered

Outputs:

    outputVolume: (an existing file name)
            Output filtered