Documentation/Nightly/Modules/GrowCutSegmentation - Revision history

Grundlett: Text replacement - "\[https:\/\/www\.slicer\.org\/slicerWiki\/index.php\/([^ ]+) ([^]]+)]" to "$2"

2019-11-27T02:40:06Z

Text replacement - "\[https:\/\/www\.slicer\.org\/slicerWiki\/index.php\/([^ ]+) ([^]]+)]" to "$2"

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2019-11-27T02:12:26Z

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Hillarylia: Added screenshots

2016-06-15T14:02:32Z

Added screenshots

Hillarylia: Added similar effects and references. Further updates to come

2016-06-14T16:23:05Z

Added similar effects and references. Further updates to come

Hillarylia: Began new GrowCut effect page to be linked into editor module page

2016-06-14T15:50:28Z

Began new GrowCut effect page to be linked into editor module page

New page

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{{documentation/{{documentation/version}}/module-header}}


{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
Authors: Harini Veeraraghavan, Jim Miller
Contacts:
* veerarag@ge.com


{{documentation/{{documentation/version}}/module-section|Module Description}}
{|
|
Grow Cut Segmentation is a competitive region growing algorithm using cellular automata. The algorithm works by using a set of user input scribbles for foreground and background. For N-class segmentation, the algorithm requires a set of scribbles corresponding the N classes and a scribble for a don't care class. The algorithm executes as follows:
Using the "user input scribbles", the algorithm automatically computes a region of interest that encompass the scribbles. It is important that the scribbles do not include a single voxel out of your region of interest as the
Next, the algorithm iteratively tries to label all the pixels in the image using the label of pixels in the user scribbled portions of the image.
The algorithm converges when all the pixels in the ROI are labeled, and no pixel can change it's label any more.
Individual pixels are labeled by computing a weighted similarity metric of a pixel with all its neighbors, where the weights correspond to the neighboring pixel's strength. The neighbor that results in the largest weight greater than the given pixel's strength, confers its label to the given pixel.
After the segmentation, the user can edit the segmentation by providing additional gestures in the image as illustrated in the figure below.
|
|}


{{documentation/{{documentation/version}}/module-section|Similar Modules}}


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 {{documentation/{{documentation/version}}/module-section|Similar Effects}}
-See also the [https://www.slicer.org/slicerWiki/index.php/Documentation/4.3/Modules/FastGrowCut Fast GrowCut] documentation. This is a fast version of the GrowCut algorithm. It is downloadable in the Extension Manager, thus requires memory.
+See also the [[Documentation/4.3/Modules/FastGrowCut|Fast GrowCut]] documentation. This is a fast version of the GrowCut algorithm. It is downloadable in the Extension Manager, thus requires memory.
 <!-- ---------------------------- -->

@@ Line 15: / Line 15: @@
 {|
+|
-Grow Cut Segmentation is a competitive region growing algorithm using cellular automata found as an effect in the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/Editor Editor module]. The algorithm works by using a set of user input scribbles for the foreground and background. For N-class segmentation, the algorithm requires a set of scribbles corresponding the N classes and a scribble for a don't care class.
+Grow Cut Segmentation is a competitive region growing algorithm using cellular automata found as an effect in the [[Documentation/Nightly/Modules/Editor|Editor module]]. The algorithm works by using a set of user input scribbles for the foreground and background. For N-class segmentation, the algorithm requires a set of scribbles corresponding the N classes and a scribble for a don't care class.
 The algorithm executes as follows:

@@ Line 15: / Line 15: @@
 {|
+|
-Grow Cut Segmentation is a competitive region growing algorithm using cellular automata. The algorithm works by using a set of user input scribbles for foreground and background. For N-class segmentation, the algorithm requires a set of scribbles corresponding the N classes and a scribble for a don't care class.
+Grow Cut Segmentation is a competitive region growing algorithm using cellular automata found as an effect in the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/Editor Editor module]. The algorithm works by using a set of user input scribbles for the foreground and background. For N-class segmentation, the algorithm requires a set of scribbles corresponding the N classes and a scribble for a don't care class.
 The algorithm executes as follows:
-Using the "user input scribbles", the algorithm automatically computes a region of interest that encompass the scribbles. It is important that the scribbles do not include a single voxel out of your region of interest as the seeding is sensitive and will not respond well to outliers.
+Using the "user input scribbles", the algorithm automatically computes a region of interest that encompass the scribbles. ''It is important that the scribbles do not include a single voxel out of your region of interest as the seeding is sensitive and will not respond well to outliers.'' Next, the algorithm iteratively tries to label all the pixels in the image using the label of pixels in the user scribbled portions of the image. The algorithm converges when all the pixels in the ROI are labeled, and no pixel can change it's label any more. Individual pixels are labeled by computing a weighted similarity metric of a pixel with all its neighbors, where the weights correspond to the neighboring pixel's strength. The neighbor that results in the largest weight greater than the given pixel's strength, confers its label to the given pixel. After the segmentation, the user can edit the segmentation by providing additional gestures in the image as illustrated in the figure below.
-Next, the algorithm iteratively tries to label all the pixels in the image using the label of pixels in the user scribbled portions of the image.
+<!-- ---------------------------- -->
-Individual pixels are labeled by computing a weighted similarity metric of a pixel with all its neighbors, where the weights correspond to the neighboring pixel's strength. The neighbor that results in the largest weight greater than the given pixel's strength, confers its label to the given pixel.
+<gallery widths=250px heights=250px class="center">
-After the segmentation, the user can edit the segmentation by providing additional gestures in the image as illustrated in the figure below.
+File:GrowCutInputPanel-2016-06-14.png|Input Panel, select the GrowCut icon
+File:GrowCutScribblePos-2016-06-15.png|Scribble Positive Gestures
-|}
+File:GrowCutScribbleNeg-2016-06-15.png|Scribble Negative Gestures with a different label value