.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.slicer.legacy.segmentation
=====================================


.. _nipype.interfaces.slicer.legacy.segmentation.OtsuThresholdSegmentation:


.. index:: OtsuThresholdSegmentation

OtsuThresholdSegmentation
-------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/legacy/segmentation.py#L22>`__

Wraps command **OtsuThresholdSegmentation **

title: Otsu Threshold Segmentation

category: Legacy.Segmentation

description: This filter creates a labeled image from a grayscale image. First, it calculates an optimal threshold that separates the image into foreground and background. This threshold separates those two classes so that their intra-class variance is minimal (see http://en.wikipedia.org/wiki/Otsu%27s_method). Then the filter runs a connected component algorithm to generate unique labels for each connected region of the foreground. Finally, the resulting image is relabeled to provide consecutive numbering.

version: 1.0

documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/OtsuThresholdSegmentation

contributor: Bill Lorensen (GE)

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.

Inputs::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        brightObjects: (a boolean)
                Segmenting bright objects on a dark background or dark objects on a
                bright background.
                flag: --brightObjects
        environ: (a dictionary with keys which are a value of type 'str' and
                 with values which are a value of type 'str', nipype default value:
                 {})
                Environment variables
        faceConnected: (a boolean)
                This is an advanced parameter. Adjacent voxels are face connected.
                This affects the connected component algorithm. If this parameter is
                false, more regions are likely to be identified.
                flag: --faceConnected
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        inputVolume: (an existing file name)
                Input volume to be segmented
                flag: %s, position: -2
        minimumObjectSize: (an integer)
                Minimum size of object to retain. This parameter can be used to get
                rid of small regions in noisy images.
                flag: --minimumObjectSize %d
        numberOfBins: (an integer)
                This is an advanced parameter. The number of bins in the histogram
                used to model the probability mass function of the two intensity
                distributions. Small numbers of bins may result in a more
                conservative threshold. The default should suffice for most
                applications. Experimentation is the only way to see the effect of
                varying this parameter.
                flag: --numberOfBins %d
        outputVolume: (a boolean or a file name)
                Output filtered
                flag: %s, position: -1

Outputs::

        outputVolume: (an existing file name)
                Output filtered
