interfaces.niftyseg.stats¶
BinaryStats¶
Wraps command seg_stats
Interface for executable seg_stats from NiftySeg platform.
Interface to use any binary statistical operations that can be performed
with the seg_stats command-line program.
See below for those operations:
p - <float> - The <float>th percentile of all voxels intensity (float=[0,100])
sa - <ax> - Average of all voxels
ss - <ax> - Standard deviation of all voxels
svp - <ax> - Volume of all probabilsitic voxels (sum(<in>) * <volume per voxel>)
al - <in2> - Average value in <in> for each label in <in2>
d - <in2> - Calculate the Dice score between all classes in <in>and <in2>
ncc - <in2> - Normalized cross correlation between <in> and <in2>
nmi - <in2> - Normalized Mutual Information between <in> and <in2>
Vl - <csv> - Volume of each integer label <in>. Save to <csv>file.
Nl - <csv> - Count of each label <in>. Save to <csv> file.
Examples¶
>>> import copy
>>> from nipype.interfaces import niftyseg
>>> binary = niftyseg.BinaryStats()
>>> binary.inputs.in_file = 'im1.nii'
>>> # Test sa operation
>>> binary_sa = copy.deepcopy(binary)
>>> binary_sa.inputs.operation = 'sa'
>>> binary_sa.inputs.operand_value = 2.0
>>> binary_sa.cmdline
'seg_stats im1.nii -sa 2.00000000'
>>> binary_sa.run() # doctest: +SKIP
>>> # Test ncc operation
>>> binary_ncc = copy.deepcopy(binary)
>>> binary_ncc.inputs.operation = 'ncc'
>>> binary_ncc.inputs.operand_file = 'im2.nii'
>>> binary_ncc.cmdline
'seg_stats im1.nii -ncc im2.nii'
>>> binary_ncc.run() # doctest: +SKIP
>>> # Test Nl operation
>>> binary_nl = copy.deepcopy(binary)
>>> binary_nl.inputs.operation = 'Nl'
>>> binary_nl.inputs.operand_file = 'output.csv'
>>> binary_nl.cmdline
'seg_stats im1.nii -Nl output.csv'
>>> binary_nl.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (an existing file name)
image to operate on
flag: %s, position: 2
operand_file: (an existing file name)
second image to perform operation with
flag: %s, position: 5
mutually_exclusive: operand_value
operand_value: (a float)
value to perform operation with
flag: %.8f, position: 5
mutually_exclusive: operand_file
operation: ('p' or 'sa' or 'ss' or 'svp' or 'al' or 'd' or 'ncc' or
'nmi' or 'Vl' or 'Nl')
operation to perform
flag: -%s, position: 4
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
larger_voxel: (a float)
Only estimate statistics if voxel is larger than <float>
flag: -t %f, position: -3
mask_file: (an existing file name)
statistics within the masked area
flag: -m %s, position: -2
Outputs:
output: (an array)
Output array from seg_stats
StatsCommand¶
Wraps command seg_stats
Base Command Interface for seg_stats interfaces.
The executable seg_stats enables the estimation of image statistics on continuous voxel intensities (average, standard deviation, min/max, robust range, percentiles, sum, probabilistic volume, entropy, etc) either over the full image or on a per slice basis (slice axis can be specified), statistics over voxel coordinates (location of max, min and centre of mass, bounding box, etc) and statistics over categorical images (e.g. per region volume, count, average, Dice scores, etc). These statistics are robust to the presence of NaNs, and can be constrained by a mask and/or thresholded at a certain level.
Inputs:
[Mandatory]
in_file: (an existing file name)
image to operate on
flag: %s, position: 2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
larger_voxel: (a float)
Only estimate statistics if voxel is larger than <float>
flag: -t %f, position: -3
mask_file: (an existing file name)
statistics within the masked area
flag: -m %s, position: -2
Outputs:
output: (an array)
Output array from seg_stats
UnaryStats¶
Wraps command seg_stats
Interface for executable seg_stats from NiftySeg platform.
Interface to use any unary statistical operations that can be performed
with the seg_stats command-line program.
See below for those operations:
r - The range <min max> of all voxels.
R - The robust range (assuming 2% outliers on both sides) of all voxels
a - Average of all voxels
s - Standard deviation of all voxels
v - Volume of all voxels above 0 (<# voxels> * <volume per voxel>)
vl - Volume of each integer label (<# voxels per label> * <volume per voxel>)
vp - Volume of all probabilsitic voxels (sum(<in>) * <volume per voxel>)
n - Count of all voxels above 0 (<# voxels>)
np - Sum of all fuzzy voxels (sum(<in>))
e - Entropy of all voxels
ne - Normalized entropy of all voxels
x - Location (i j k x y z) of the smallest value in the image
X - Location (i j k x y z) of the largest value in the image
c - Location (i j k x y z) of the centre of mass of the object
B - Bounding box of all nonzero voxels [ xmin xsize ymin ysize zmin zsize ]
xvox - Output the number of voxels in the x direction. Replace x with y/z for other directions.
xdim - Output the voxel dimention in the x direction. Replace x with y/z for other directions.
Examples¶
>>> import copy
>>> from nipype.interfaces import niftyseg
>>> unary = niftyseg.UnaryStats()
>>> unary.inputs.in_file = 'im1.nii'
>>> # Test v operation
>>> unary_v = copy.deepcopy(unary)
>>> unary_v.inputs.operation = 'v'
>>> unary_v.cmdline
'seg_stats im1.nii -v'
>>> unary_v.run() # doctest: +SKIP
>>> # Test vl operation
>>> unary_vl = copy.deepcopy(unary)
>>> unary_vl.inputs.operation = 'vl'
>>> unary_vl.cmdline
'seg_stats im1.nii -vl'
>>> unary_vl.run() # doctest: +SKIP
>>> # Test x operation
>>> unary_x = copy.deepcopy(unary)
>>> unary_x.inputs.operation = 'x'
>>> unary_x.cmdline
'seg_stats im1.nii -x'
>>> unary_x.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (an existing file name)
image to operate on
flag: %s, position: 2
operation: ('r' or 'R' or 'a' or 's' or 'v' or 'vl' or 'vp' or 'n' or
'np' or 'e' or 'ne' or 'x' or 'X' or 'c' or 'B' or 'xvox' or
'xdim')
operation to perform
flag: -%s, position: 4
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
larger_voxel: (a float)
Only estimate statistics if voxel is larger than <float>
flag: -t %f, position: -3
mask_file: (an existing file name)
statistics within the masked area
flag: -m %s, position: -2
Outputs:
output: (an array)
Output array from seg_stats