Modules:Simple Region Growing-Documentation-3.6 - Revision history

Millerjv at 22:19, 28 April 2010

2010-04-28T22:19:33Z

Millerjv: /* Authors, Collaborators & Contact */

2010-04-28T22:19:14Z

Authors, Collaborators & Contact

Harveerar: /* Source code & documentation */

2010-04-27T13:22:01Z

Source code & documentation

Harveerar: /* Dependencies */

2010-04-27T13:21:06Z

Dependencies

Harveerar: /* Examples, Use Cases & Tutorials */

2010-04-27T13:20:21Z

Examples, Use Cases & Tutorials

Harveerar: /* Examples, Use Cases & Tutorials */

2010-04-27T13:19:59Z

Examples, Use Cases & Tutorials

Harveerar: /* Examples, Use Cases & Tutorials */

2010-04-27T13:19:43Z

Examples, Use Cases & Tutorials

Harveerar: /* Usage */

2010-04-27T12:29:54Z

Usage

Harveerar: /* Module Description */

2010-04-27T12:27:49Z

Module Description

Harveerar: /* Quick Tour of Features and Use */

2010-04-26T19:04:51Z

Quick Tour of Features and Use

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 [[Documentation-3.6|Return to Slicer 3.6 Documentation]]
 __NOTOC__
 ===Module Name===

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 ===Authors, Collaborators & Contact===
 * Author: Jim Miller
-* Contact: millerjv at research.ge.com
+* Contact: millerjv at ge.com
 ===Module Description===

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 ===Source code & documentation===
-Source Code: [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/]
+*Source Code: [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/ConfidenceConnected.cxx ConfidenceConnected.cxx]
 Documentation:

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 ===Dependencies===
-Other modules or packages that are required for this module's use.
+This module makes use of the ITKConfidenceConnectedImageFilter and ITKCurvatureFlowImageFilter.
 ===Known bugs===

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 ===Examples, Use Cases & Tutorials===
 * Useful for segmenting out regions of interest in a volume (image)
-* Example Test: Shows an example of segmenting the brain ventricles in an MR image
+* Example Test: Shows an example of segmenting the brain ventricles in an MR volume of brain
 [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/Testing/ConfidenceConnectedTest.cxx ConfidenceConnectedTest.cxx]

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 ===Examples, Use Cases & Tutorials===
+* Useful for segmenting out regions of interest in a volume (image)
-* Note use cases for which this module is especially appropriate, and/or link to examples.
+* Example Test: Shows an example of segmenting the brain ventricles in an MR image
-* Link to examples of the module's use
+*[http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/Testing/ConfidenceConnectedTest.cxx ConfidenceConnectedTest.cxx]
 ===Quick Tour of Features and Use===

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 == Usage ==
-* Set the parameters for smoothing. The smoothing parameters are '''Smoothing iterations''' and the '''Time step'''. The smoothing parameters correspond to the parameters of the itkCurvatureFlowImageFilter where Smoothing iterations specifies the number of iterations to perform the smoothing and the time step parameter specifies the time step between the different level set contour. In general use a small time step parameter for stability of smoothing.
+* Set the parameters for smoothing. The smoothing parameters are '''Smoothing iterations''' and the '''Time step'''. The smoothing parameters correspond to the parameters of the itkCurvatureFlowImageFilter where smoothing iterations specifies the number of iterations to perform the smoothing and the time step parameter specifies the time step between the different level set contour. In general use a small time step parameter for stability of smoothing.
 * Set the parameters for segmentation. The segmentation parameters correspond to those of the itkConfidenceConnectedImageFilter. The '''Number of iterations''' corresponds to the number of repetitions for recomputing the segmentation, the '''Multiplier''' parameter corresponds to the standard deviation from the mean intensity based on the seed point voxel. In general a setting of 2.5 ensures that 99% of samples are captured. The parameter '''Neighborhood Radius''' corresponds to the neighborhood around the seed point that will be evaluated. Setting this parameter to 1 corresponds to 8 neighbors for 2D and 26 neighbors for 3D image.
 * Specify the input volume

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 ===Module Description===
 Simple Region Growing is a statistical region growing algorithm.  The algorithm takes one or more '''seeds''' as input. It executes using the following steps:
-* A statistical model of the foreground (mean and standard deviation of intensity) is estimated over neighborhoods about the seed points. The statistical model is converted to a '''scalar threshold range''' using the mean intensity of a seed point plus or minus a '''multiplier''' or the '''standard deviation'''.
+* A statistical model of the foreground (mean and standard deviation of intensity) is estimated over neighborhoods of the seed points. The statistical model is converted to a '''scalar threshold range''' using the mean intensity of a seed point plus or minus a '''multiplier''' or the '''standard deviation'''.
 * The algorithm then constructs a segmentation by labeling all voxels that are connected to the seed voxels and satisfy the scalar threshold range.

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 ===Quick Tour of Features and Use===
 List all the panels in your interface, their features, what they mean, and how to use them. For instance:
 * '''Smoothing parameters panel:'''
   * Smoothing iterations: Specifies the number of iterations for performing the smoothing
   * Time step: Specifies the time step between the iterations. In general use a smaller setting of the time step.
 * '''Segmentation parameters panel:'''
-  * Number of iterations: Number of iterations for repeating the segmentation
+ * Number of iterations: Number of iterations for repeating the segmentation
-  * Multiplier: Number of standard deviations from the mean intensity computed on the seed points
+ * Multiplier: Number of standard deviations from the mean intensity computed on the seed points
-  * Neighborhood Radius: Distance of neighboring voxels (pixels) to use from the center pixel (seed point)
+ * Neighborhood Radius: Distance of neighboring voxels (pixels) to use from the center pixel (seed point)
-  * Output Label Value: Intensity assigned to the voxels (pixels) segmented as foreground
+ * Output Label Value: Intensity assigned to the voxels (pixels) segmented as foreground
 * '''IO panel:'''
-  * Input Volume: Input
+ * Input Volume: Input
-  * Output Volume: Output
+ * Output Volume: Output
 == Development ==