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

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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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The Sobolev segmenter is a general image segmenter, and it 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]]
 
# Load the image (input volume):  [[File:DICOM_example.jpg|200 px]]
# 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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# Use built in editor to select a single initial mask (or load a binary mask file): [[File:mask_example.jpg|200 px]]
 
# 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.
 
# Choose the Input Volume and the Initial Mask accordingly. Create a new volume for the Output Volume.

Revision as of 03:16, 25 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, <email>nakhmani@gmail.com</email>

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 a general image segmenter, and it 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 a single 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