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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]
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    brightObjects: (a boolean)
            Segmenting bright objects on a dark background or dark objects on a bright background.
    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.
    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
    minimumObjectSize: (an integer)
            Minimum size of object to retain. This parameter can be used to get rid of small regions
            in noisy images.
    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.
    outputVolume: (a boolean or a file name)
            Output filtered

Outputs:

    outputVolume: (an existing file name)
            Output filtered