Difference between revisions of "Documentation/Nightly/Modules/SobolevSegmenter"

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This extension implements Sobolev inner product based active contour, using Chan-Vese energy functional. The segmentation is appropriate for 2D images. The obtained parametric contour is generally smooth, but able to catch concavities.
 
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The Sobolev segmenter is general and can be used with any 2D data, as explained in the tutorial.
  
 
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# Load the image (input volume):  [[File:DICOM_example.jpg|200 px]]
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# Use built in editor to select an initial mask (or load a binary mask file): [[File:mask_example.jpg|200 px]]
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# Select Segmenation->SobolevSegmenter module
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# Choose the Input Volume and the Initial Mask accordingly. Create a new volume for the Output Volume.
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# Press Apply button.
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# After a few second the following output volume should appear: [[File:Output_example.png|200 px]]
  
 
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The module has the following panel:
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[[File:Sobolev_panel.png]]
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The IO section of this panel defines two input images (data and initial mask) and one output image (final mask). The algorithm has three parameters: self-explanatory number of iterations and contour evolution step size. In addition, the parameter lambda chooses the smoothness of the contour (smoothing kernel width).
 
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Revision as of 22:10, 24 February 2013

Home < Documentation < Nightly < Modules < SobolevSegmenter

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. Information on NA-MIC can be obtained from the NA-MIC website.
Author: Arie Nakhmani, UAB
Contributor1: Allen Tannenbaum, UAB
Contact: Arie Nakhmani,

University of Alabama at Birmingham  

Module Description

This extension implements Sobolev inner product based active contour, using Chan-Vese energy functional. The segmentation is appropriate for 2D images. The obtained parametric contour is generally smooth, but able to catch concavities.

Use Cases

The Sobolev segmenter is general and can be used with any 2D data, as explained in the tutorial.

Tutorials

  1. Load the image (input volume): DICOM example.jpg
  2. Use built in editor to select an initial mask (or load a binary mask file): Mask example.jpg
  3. Select Segmenation->SobolevSegmenter module
  4. Choose the Input Volume and the Initial Mask accordingly. Create a new volume for the Output Volume.
  5. Press Apply button.
  6. After a few second the following output volume should appear: Output example.png

Panels and their use

The module has the following panel: Sobolev panel.png The IO section of this panel defines two input images (data and initial mask) and one output image (final mask). The algorithm has three parameters: self-explanatory number of iterations and contour evolution step size. In addition, the parameter lambda chooses the smoothness of the contour (smoothing kernel width).

Similar Modules

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References

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Information for Developers