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Revision as of 23:01, 20 October 2011

Home < Documentation < 4.0 < Modules < GradientAnisotropicDiffusion

Introduction and Acknowledgements

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: Jim Miller, <email>miller@ge.com</email>


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.

Use Cases

Most frequently used for these scenarios:

  • Use Case 1: Noise reduction as a preprocessing step for segmentation
    • when dealing with single voxel classification schemes running noise reduction as a preprocessing scheme will reduce the number of single misclassified voxels.
  • Use Case 2: Preprocessing to volume rendering
    • Noise reduction will result in nicer looking volume renderings


Links to tutorials that use this module




  • Point to other modules that have similar functionality


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