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# interfaces.slicer.legacy.segmentation

## OtsuThresholdSegmentation

Link to code

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]

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

Outputs:

    outputVolume: (an existing file name)
            Output filtered