Difference between revisions of "Documentation/4.0/Modules/GradientAnisotropicDiffusion"

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==Module Description==
 
==Module Description==
Here comes a description what the module is good for. Explain briefly how it works and point to the [[Module:EndUserDocumentationTemplate-4.0#References|references]] giving more details on the algorithm.
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Runs gradient anisotropic diffusion on a volume.
  
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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.
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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.
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[[Module:EndUserDocumentationTemplate-4.0#References|references]] giving more details on the algorithm.
  
 
==Use Cases==
 
==Use Cases==

Revision as of 15:45, 11 August 2011

Home < Documentation < 4.0 < Modules < GradientAnisotropicDiffusion

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Gallery of New Features


Introduction and Acknowledgements

MyModule: Put the name of the module here
  • Author1: Steve Pieper, Isomics (replace this with your name and affiliation)
  • Contributor1: Nicole Aucoin, Surgical Planning Laboratory, BWH, HMS (replace this with your name and affiliation)
  • Contributor2: Name, Affiliation
  • Contact: name, email: This is required
Replace this logo with yours
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Module 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.

references giving more details on the algorithm.

Use Cases

Most frequently used for these scenarios:

  • Use Case 1:
  • Use Case 2:

Tutorials

Links to tutorials that use this module

Panels and their use

A list of all the panels in the interface, their features, what they mean, and how to use them. For instance:

  • Input panel1:
    • First input
    • Second input
  • Parameters panel:
    • First parameter
    • Second parameter
  • Output panel:
    • First output
    • Second output
  • Viewing panel:
Name of panel 1
  • Input panel2:
    • First input
    • Second input
  • Parameters panel:
    • First parameter
    • Second parameter
  • Output panel:
    • First output
    • Second output
  • Viewing panel:
Name of panel 2

Similar Modules

  • Point to other modules that have similar functionality

References

Publications related to this module go here. Links to pdfs would be useful. For extensions: link to the source code repository and additional documentation

Information for Developers

Module Type & Category

Type: Interactive or CLI

Category: Base or (Filtering, Registration, etc.)

Notes from the Developer(s)

Algorithms used, library classes depended upon, use cases, etc.

Dependencies

Other modules or packages that are required for this module's use.

Tests

On the Slicer4 Dashboard, these tests verify that the module is working on various platforms:

Source code & documentation

Links to the module's source code:

Source code:

Doxygen documentation: