Documentation/4.0/Modules/GradientAnisotropicDiffusion

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Introduction and Acknowledgements

GradientAnisotropicFilter	This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website. Contact: millerjv at ge.crd	NA-MIC
		ITK

Module Description

Runs the ITK gradient anisotropic diffusion filter 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.

See references for more details on the algorithm.

Use Cases

Most frequently used for these scenarios:

• Use Case 1: Noise reduction as a preprocessing step for segmentation
• Use Case 2: Preprocessing to volume rendering

Tutorials

Links to tutorials that use this module

Panels

Parameters: Conductance 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. (default: 1) Iterations 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. (default: 5) TimeStep The time step depends on the dimensionality of the image. In Slicer4 the images are 3D and the default (.0625) time step will provide a stable solution. (default: 0.0625) I/O Make sure that Output is different from input If necessary rename the output file

GradientAnisotropicDiffusion

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:

• Test source code directory for GradientAnisotropicDiffusion

Source code & documentation

Links to the module's source code:

Source code:

• GradientAnisotropicDiffusionCode

Doxygen documentation:

• ITK Code