Modules:FuzzySegmentationModule
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Module Name
Fuzzy Tissue Classification
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General Information
Module Type & Category
Type: CLI
Category: Segmentation
Authors, Collaborators & Contact
- Author: Xiaodong Tao
- Contact: taox at research.ge.com
Module Description
This module computes voxel by voxel tissue classification of an MR brain image using a fuzzy c-means algortihm. Bias field is modeled as a lower order polynomial. Bias field and tissue classification are estimated iteratively in an EM fashion. Internally, each voxel is assigned tissue membership function values, which range from 0 to 1. At any voxel, the sum of membership function of all classes is either 0 (outside of brain), or 1. The membership functions are converted in tissue labels to generate hard segmentation.
Usage
Examples, Use Cases & Tutorials
Quick Tour of Features and Use
Development
Dependencies
Known bugs
None.
Usability issues
None.
Source code & documentation
Source Code:
XML Description:
Usage:
USAGE:
../Slicer3-ext/BrainTissueClassification-build/lib/Slicer3/Plugins/Tissu
eClassification
[--returnparameterfile
<std::string>]
[--processinformationaddress
<std::string>] [--xml] [--echo] [-b
<int>] [-c <int>] [--] [--version]
[-h] <std::string> <std::string>
<std::string> <std::string>
Where:
--returnparameterfile <std::string>
Filename in which to write simple return parameters (int, float,
int-vector, etc.) as opposed to bulk return parameters (image,
geometry, transform, measurement, table).
--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)
-b <int>, --biasoption <int>
Option for bias correction (0: no bias correction; 1: global linear;
2: global quadratic; 3: region based linear; 4: region based
quadratic) (default: 0)
-c <int>, --class <int>
Number of classes (default: 3)
--, --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 T1 Image.
<std::string>
(required) Only voxels inside the mask are classified
<std::string>
(required) Output brain mask map.
<std::string>
(required) Estimated bias field
Description: This module computes voxel by voxel tissue classification
of an MR brain image using a fuzzy c-means algortihm. Bias field is
modeled as a lower order polynomial. Bias field and tissue
classification are estimated iteratively in an EM fashion. Internally,
each voxel is assigned tissue membership function values, which range
from 0 to 1. At any voxel, the sum of membership function of all classes
is either 0 (outside of brain), or 1. The membership functions are
converted in tissue labels to generate hard segmentation.
Author(s): Xiaodong Tao, taox @ research . ge . com
Acknowledgements: 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.
Implementation of the Fuzzy Classification was contributed by Dr.
Ming-Ching Chang from GE Research.
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. Implementation of the Fuzzy Classification was contributed by Dr. Ming-Ching Chang from GE Research.
References
- Ming-ching Chang, Xiaodong Tao “Subvoxel Segmentation and Representation of Brain Cortex Using Fuzzy Clustering and Gradient Vector Diffusion”, SPIE Medical Imaging, San Diego, CA, 2010.
