Modules:OtsuThreshold-Documentation-3.4
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Module Name
Otsu Threshold
General Information
Module Type & Category
Type: CLI
Category: Filtering
Authors, Collaborators & Contact
- Author: Bill Lorensen
- Contact: bill.lorensen at gmail.com
Module Description
Overview of what the module does goes here.
Usage
Examples, Use Cases & Tutorials
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Development
Dependencies
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Source code & documentation
Source Code: OtsuThreshold.cxx
XML Description: OtsuThreshold.xml
Usage:
./OtsuThresholdImageFilter [--processinformationaddress <std::string>] [--xml] [--echo] [--numberOfBins <int>] [--outsideValue <int>] [--insideValue <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) --numberOfBins <int> The number of bins in the histogram (default: 128) --outsideValue <int> The value assigned to pixels that are outside the computed threshold (default: 255) --insideValue <int> The value assigned to pixels that are inside the computed threshold (default: 0) --, --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: This filter creates a binary thresholded image that separates an image into foreground and background components. The filter computes the threshold using the algorthm described in 'xxx', applies that threshold to the input image using the BinaryThresholdImageFilter. The NumberOfHistogram bins can be set for the Otsu Calculator. The insideValue and outsideValue can be set for the BinaryThresholdImageFilter. Author(s): Bill Lorensen 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.