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. | + | 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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{{documentation/{{documentation/version}}/module-section|Tutorials}} | {{documentation/{{documentation/version}}/module-section|Tutorials}} | ||
# 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 | + | # 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 | # Select Segmenation->SobolevSegmenter module | ||
# 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 < SobolevSegmenterIntroduction and Acknowledgements
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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. | |||
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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
- Load the image (input volume):
- Use built in editor to select a single initial mask (or load a binary mask file):
- Select Segmenation->SobolevSegmenter module
- Choose the Input Volume and the Initial Mask accordingly. Create a new volume for the Output Volume.
- Press Apply button.
- After a few second the following output volume should appear:
Panels and their use
The module has the following panel:
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
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Section under construction. |
