class HistogramMaskInputSpec(BaseInterfaceInputSpec):
in_file = traits.File(desc="Input Image", exists=True, mandatory=True)
out_file = traits.File(name_template='%s_histo_mask',
name_source='in_file',
keep_extension=True,
desc="Output Image")
volume_threshold = traits.Int(1650,
desc="Volume threshold. [default: 1650]",
usedefault=True)
intensity_threshold = traits.Int(desc="Intensity threshold. "
"[default: 500]")
terminal_output = traits.Enum(
'stream',
'allatonce',
'file',
'none',
deprecated='1.0.0',
desc=('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'),
nohash=True)
lower_cutoff = traits.Float(
.2,
desc="lower fraction of the histogram to be discarded. In case of "
"failure, it is usually advisable to increase lower_cutoff "
"[default: 0.2]",
usedefault=True)
upper_cutoff = traits.Float(
.85,
desc="upper fraction of the histogram to be discarded."
"[default: 0.85]",
usedefault=True)
connected = traits.Bool(True,
desc="keep only the largest connect component",
usedefault=True)
opening = traits.Int(
5,
desc="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]",
usedefault=True)
closing = traits.Int(
0,
desc="Number of binary closing iterations to post-process the mask. "
"[default: 10]",
usedefault=True)
dilation_size = traits.Tuple(
(1, 1, 2),
desc="Element size for binary dilation if needed",
usedefault=True)
verbose = traits.Bool(False, desc="be very verbose", usedefault=True)
class MathMorphoMaskInputSpec(CommandLineInputSpec):
in_file = traits.File(desc="Input Image",
exists=True,
mandatory=True,
argstr="%s",
position=0)
out_file = traits.File(desc="Output Image",
name_template='%s_morpho_mask',
name_source='in_file',
keep_extension=True,
argstr="%s",
position=1)
volume_threshold = traits.Int(
desc="Volume threshold (the parameter V in the paper). "
"[default: 1650]",
argstr="-v %s")
intensity_threshold = traits.Int(
desc="Intensity threshold (the parameter T in the paper). "
"[default: 500]",
argstr="-t %s")
class MathMorphoMaskOutputSpec(TraitedSpec):
out_file = traits.File(desc="Brain mask file", exists=True)
class HistogramMaskOutputSpec(TraitedSpec):
out_file = traits.File(desc="Brain mask file", exists=True)
class RatsMMOutputSpec(TraitedSpec):
out_file = traits.File(desc="Brain masked file", exists=True)