Documentation/Nightly/Modules/SobolevSegmenter - Revision history

Grundlett: Text replacement - "\[http:\/\/www\.slicer\.org\/slicerWiki\/images\/([^ ]+) ([^]]+)]" to "[http://www.slicer.org/w/img_auth.php/$1 $2]"

2019-11-27T12:38:48Z

Text replacement - "\[http:\/\/www\.slicer\.org\/slicerWiki\/images\/([^ ]+) ([^]]+)]" to "[http://www.slicer.org/w/img_auth.php/$1 $2]"

Anry123 at 02:38, 27 November 2013

2013-11-27T02:38:56Z

UpdateBot: Prepend documentation/versioncheck template. See http://na-mic.org/Mantis/view.php?id=2887

2013-06-14T07:50:11Z

Prepend documentation/versioncheck template. See http://na-mic.org/Mantis/view.php?id=2887

Anry123 at 17:48, 25 February 2013

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Anry123 at 17:46, 25 February 2013

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Anry123 at 03:17, 25 February 2013

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Anry123 at 03:16, 25 February 2013

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Anry123 at 03:10, 25 February 2013

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Anry123 at 02:34, 25 February 2013

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Anry123 at 02:28, 25 February 2013

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 The 3D volume segmentation tutorial:
-[http://www.slicer.org/slicerWiki/images/f/f7/Sobolev.mp4]
+[http://www.slicer.org/w/img_auth.php/f/f7/Sobolev.mp4]
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 # Load the image (input volume):  [[File:DICOM_example.jpg|200 px]]
 # Use built in editor to select a single initial mask (or load a binary mask file): [[File:mask_example.jpg|200 px]]
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 # Press Apply button.
 # After a few second the following output volume should appear: [[File:Output_example.png|200 px]]
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 {{documentation/{{documentation/version}}/module-section|Use Cases}}
-The Sobolev segmenter is a general image segmenter, and it 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 type of data, as explained in the tutorial.
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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 [http://www.na-mic.org/ NA-MIC website].<br>
+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 through grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br>
 Author: Arie Nakhmani, UAB<br>
 Contributor1: Allen Tannenbaum, UAB<br>
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 {{documentation/{{documentation/version}}/module-section|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.
+This extension implements Sobolev inner product based active contour, using the Chan-Vese energy functional. The segmentation is appropriate for 2D images. Extensions are being made now to the 3D case and will be available in the near future. The parametric contour is generally smooth, and able to capture concavities.
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 The module has the following panel:
 [[File: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).
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-|{{collaborator|logo|UAB}}|{{collaborator|longname|UAB}}
+|Image:UAB_logo.png|University of Alabama at Birmingham
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