Modules:Simple Region Growing-Documentation-3.6

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Module Name

Simple Region Growing

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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. 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.
• 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 iteratively refined by re-calculating the mean and standard deviation of the intensity of the voxels in the initial segmentation. The refined statistical model in turn is converted to a new scalar threshold range as described in the preceding paragraph. This is followed by a new segmentation where the algorithm labels all voxels connected to the seed voxels and satisfy the new scalar threshold range. The number of repetitions for the segmentation process is specified using an iteration parameter to the algorithm.

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

Examples, Use Cases & Tutorials

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Development

Dependencies

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Source code & documentation

Source Code: [1]

Documentation:

Usage:

  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

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 National Centers for Biomedical Computing.