5.6.1. segmentation.interfaces

5.6.1.1. HistogramMask

Link to code

Interface for nilearn.masking.compute_epi_mask, based on T. Nichols heuristics.

5.6.1.1.1. Examples

>>> from sammba.segmentation import HistogramMask
>>> nichols_masker = HistogramMask()
>>> nichols_masker.inputs.in_file = 'structural.nii'
>>> nichols_masker.inputs.volume_threshold = 1650
>>> res = nichols_masker.run()  

Inputs:

[Mandatory]
in_file: (a file name)
        Input Image

[Optional]
closing: (an integer (int or long), nipype default value: 0)
        Number of binary closing iterations to post-process the mask.
        [default: 10]
connected: (a boolean, nipype default value: True)
        keep only the largest connect component
dilation_size: (a tuple of the form: (a value of type 'int', a value
         of type 'int', a value of type 'int'), nipype default value: (1, 1,
         2))
        Element size for binary dilation if needed
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
intensity_threshold: (an integer (int or long))
        Intensity threshold. [default: 500]
lower_cutoff: (a float, nipype default value: 0.2)
        lower fraction of the histogram to be discarded. In case of failure,
        it is usually advisable to increase lower_cutoff [default: 0.2]
opening: (an integer (int or long), nipype default value: 5)
        Order of the morphological opening to perform, to keep only large
        structures. This step is useful to remove parts of the skull that
        might have been included. If the opening order is `n` > 0, 2`n`
        closing operations are performed after estimation of the largest
        connected constituent, followed by `n` erosions. This corresponds to
        1 opening operation of order `n` followed by a closing operator of
        order `n`. Note that turning off opening (opening=0) will also
        prevent any smoothing applied to the image during the mask
        computation. [default: 2]
out_file: (a file name)
        Output Image
upper_cutoff: (a float, nipype default value: 0.85)
        upper fraction of the histogram to be discarded.[default: 0.85]
verbose: (a boolean, nipype default value: False)
        be very verbose
volume_threshold: (an integer (int or long), nipype default value:
         1650)
        Volume threshold. [default: 1650]

Outputs:

out_file: (a file name)
        Brain mask file

5.6.1.2. MathMorphoMask

Link to code

Wraps command RATS_MM

Mathemetical morphology stage for RATS, as described in: RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI. Journal of neuroscience methods (2014) vol. 221 pp. 175 - 182.

Author(s): Ipek Oguz, Milan Sonka

5.6.1.2.1. Examples

>>> from sammba.segmentation import MathMorphoMask
>>> rats_masker = MathMorphoMask()
>>> rats_masker.inputs.in_file = 'structural.nii'
>>> rats_masker.inputs.intensity_threshold = 1000
>>> rats_masker.cmdline  
'RATS_MM structural.nii structural_morpho_mask.nii -t 1000'
>>> res = rats_masker.run()  

Inputs:

[Mandatory]
in_file: (a file name)
        Input Image
        flag: %s, position: 0

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
intensity_threshold: (an integer (int or long))
        Intensity threshold (the parameter T in the paper). [default: 500]
        flag: -t %s
out_file: (a file name)
        Output Image
        flag: %s, position: 1
volume_threshold: (an integer (int or long))
        Volume threshold (the parameter V in the paper). [default: 1650]
        flag: -v %s

Outputs:

out_file: (a file name)
        Brain mask file