Modules:Simple Region Growing-Documentation-3.4 - Revision history

Marianna at 20:01, 26 May 2009

2009-05-26T20:01:27Z

Millerjv: /* Source code & documentation */

2009-02-25T20:08:12Z

Source code & documentation

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

2009-02-25T20:04:16Z

Quick Tour of Features and Use

Millerjv: /* Module Description */

2009-02-25T20:00:35Z

Module Description

Millerjv: /* Module Description */

2009-02-25T19:59:47Z

Module Description

Millerjv: New page: Return to Slicer 3.4 Documentation NOTOC ===Module Name=== Simple Region Growing {| |Caption 1 |[[Image:screenshotBlan...

2009-02-25T19:54:49Z

New page: Return to Slicer 3.4 Documentation __NOTOC__ ===Module Name=== Simple Region Growing {| |thumb|280px|Caption 1 |[[Image:screenshotBlan...

New page

[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
__NOTOC__
===Module Name===
Simple Region Growing

{|
|[[Image:screenshotBlank.png|thumb|280px|Caption 1]]
|[[Image:screenshotBlank.png|thumb|280px|Caption 2]]
|[[Image:screenshotBlank.png|thumb|280px|Caption 3]]
|}

== General Information ==
===Module Type & Category===

Type: CLI

Category: Segmentation

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

===Module Description===
Simple Region Growing is a statistical region growing algorithm. The algorithm takes one or more seeds as input. 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 as the mean intensity plus or minus a multiplier on 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. After this initial segmentation, the statistical model can be refined by calculating the mean and standard deviation of the intensity of the voxels in the initial segmentation. The refined statistical model is converted to a new scalar threshold range as the new mean intensity plus or minus a multiplier on the new standard deviation. The algorithm then constructs a new segmentation by labeling all voxels that are connected to the seed voxels and satisfy the new scalar threshold range. The process of estimating a statistical model, constructing a scalar threshold range, and constructing a segmentation from the seeds can be repeated a number of times.

== Usage ==

===Examples, Use Cases & Tutorials===

* Note use cases for which this module is especially appropriate, and/or link to examples.
* Link to examples of the module's use
* Link to any existing tutorials

===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:

* '''Input panel:'''
* '''Parameters panel:'''
* '''Output panel:'''
* '''Viewing panel:'''

== Development ==

===Dependencies===

Other modules or packages that are required for this module's use.

===Known bugs===

Follow this [http://na-mic.org/Mantis/main_page.php link] to the Slicer3 bug tracker.

===Usability issues===

Follow this [http://na-mic.org/Mantis/main_page.php link] to the Slicer3 bug tracker. Please select the '''usability issue category''' when browsing or contributing.

===Source code & documentation===

Source Code: [http://www.na-mic.org/ViewVC/index.cgi/trunk/Applications/CLI/]

Documentation:

== More Information ==

===Acknowledgment===
This work is part of the National Alliance for Medical Image
Computing (NAMIC), funded by the National Institutes of Health
through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Information on the National Centers for Biomedical Computing
can be obtained from [http://www.ncbcs.org/ National Centers for Biomedical Computing].

===References===

← Older revision		Revision as of 20:08, 25 February 2009
Line 62:		Line 62:

	Documentation:		Documentation:
		+
		+	Usage:
		+
		+	<pre>
		+	ConfidenceConnected [--processinformationaddress
		+	<std::string>] [--xml] [--echo]
		+	[--seed
		+	<std::vector<std::vector<float> >>]
		+	... [--labelvalue <int>]
		+	[--neighborhood <int>]
		+	[--multiplier <double>]
		+	[--iterations <int>] [--timestep
		+	<double>] [--smoothingIterations
		+	<int>] [--] [--version] [-h]
		+	<std::string> <std::string>
		+
		+
		+	Where:
		+
		+	--processinformationaddress <std::string>
		+	Address of a structure to store process information (progress, abort,
		+	etc.). (default: 0)
		+
		+	--xml
		+	Produce xml description of command line arguments (default: 0)
		+
		+	--echo
		+	Echo the command line arguments (default: 0)
		+
		+	--seed <std::vector<std::vector<float> >> (accepted multiple times)
		+	Seed point(s) for region growing (default: None)
		+
		+	--labelvalue <int>
		+	The integer value (0-255) to use for the segmentation results. This
		+	will determine the color of the segmentation that will be generated by
		+	the Region growing algorithm (default: 2)
		+
		+	--neighborhood <int>
		+	The radius of the neighborhood over which to calculate intensity model
		+	(default: 1)
		+
		+	--multiplier <double>
		+	Number of standard deviations to include in intensity model (default:
		+	2.5)
		+
		+	--iterations <int>
		+	Number of iterations of region growing (default: 5)
		+
		+	--timestep <double>
		+	Timestep for curvature flow (default: 0.0625)
		+
		+	--smoothingIterations <int>
		+	Number of smoothing iterations (default: 5)
		+
		+	--, --ignore_rest
		+	Ignores the rest of the labeled arguments following this flag.
		+
		+	--version
		+	Displays version information and exits.
		+
		+	-h, --help
		+	Displays usage information and exits.
		+
		+	<std::string>
		+	(required) Input volume to be filtered
		+
		+	<std::string>
		+	(required) Output filtered
		+
		+
		+	Description: A simple region growing segmentation algorithm based on
		+	intensity statistics. To create a list of fiducials (Seeds) for this
		+	algorithm, click on the tool bar icon of an arrow pointing to a
		+	starburst fiducial to enter the 'place a new object mode' and then use
		+	the fiducials module. This module uses the Slicer3 Command Line
		+	Interface (CLI) and the ITK filters CurvatureFlowImageFilter and
		+	ConfidenceConnectedImageFilter.
		+
		+	Author(s): Jim Miller
		+
		+	Acknowledgements: This command module was derived from Insight/Examples
		+	(copyright) Insight Software Consortium
		+	</pre>

	== More Information ==		== More Information ==

@@ Line 25: / Line 25: @@
 Simple Region Growing is a statistical region growing algorithm.  The algorithm takes one or more seeds as input.  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 as the mean intensity plus or minus a multiplier on 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. After this initial segmentation, the statistical model can be refined by calculating the mean and standard deviation of the intensity of the voxels in the initial segmentation.  The refined statistical model is converted to a new scalar threshold range as the new mean intensity plus or minus a multiplier on the new standard deviation. The algorithm then constructs a new segmentation by labeling all voxels that are connected to the seed voxels and satisfy the new scalar threshold range. The process of estimating a statistical model, constructing a scalar threshold range, and constructing a segmentation from the seeds can be repeated a number of times.
-Through this process, Simple Region Growing attempts to adapt to the statistical properties of the image. Initially, the statistical model is based strictly on the neighborhoods about the seeds.  This statistical model is precise (being based on the user supplied seeds) but also uncertain (because the number of samples in the model can be rather small).  After the initial segmentation, the statistics are recalculated which yields a more certain model because the number of samples in the model can be rather large).
+Through this process, Simple Region Growing attempts to adapt to the statistical properties of the image. Initially, the statistical model is based strictly on the neighborhoods about the seeds.  This statistical model is precise (being based on the user supplied seeds) but also uncertain (because the number of samples in the model can be rather small).  After the initial segmentation, the statistics are recalculated which yields a more certain model (because the number of samples in the model can be rather large).
 == Usage ==

@@ Line 1: / Line 1: @@
 [[Documentation-3.4|Return to Slicer 3.4 Documentation]]
 __NOTOC__
 ===Module Name===

@@ Line 38: / Line 38: @@
 List all the panels in your interface, their features, what they mean, and how to use them. For instance:
-* '''Input panel:'''
+* '''Smoothing parameters panel:'''
-* '''Parameters panel:'''
+* '''Segmentation parameters panel:'''
-* '''Output panel:'''
+* '''IO panel:'''
 == Development ==

Modules:Simple Region Growing-Documentation-3.4 - Revision history

Marianna at 20:01, 26 May 2009

Millerjv: /* Source code & documentation */

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

Millerjv: /* Module Description */

Millerjv: /* Module Description */

Millerjv: New page: Return to Slicer 3.4 Documentation __NOTOC__ ===Module Name=== Simple Region Growing {| |Caption 1 |[[Image:screenshotBlan...

Millerjv: New page: Return to Slicer 3.4 Documentation NOTOC ===Module Name=== Simple Region Growing {| |Caption 1 |[[Image:screenshotBlan...