ants.ops.bias_correction

Functions

`abp_n4`(image[, intensity_truncation, mask, ...])	Truncate outlier intensities and bias correct with the N4 algorithm.
`n3_bias_field_correction`(image[, ...])	N3 Bias Field Correction
`n3_bias_field_correction2`(image[, mask, ...])	N3 Bias Field Correction
`n4_bias_field_correction`(image[, mask, ...])	N4 Bias Field Correction

abp_n4(image, intensity_truncation=(0.025, 0.975, 256), mask=None, usen3=False)[source]

Truncate outlier intensities and bias correct with the N4 algorithm.

ANTsR function: abpN4

Parameters:

image (ants.core.ANTsImage) – image to correct and truncate
intensity_truncation (typing.Tuple) – quantiles for intensity truncation
mask (ANTsImage (optional)) – mask for bias correction
usen3 (bool) – if True, use N3 bias correction instead of N4

Return type:

ants.core.ANTsImage

Example

>>> import ants
>>> image = ants.image_read(ants.get_ants_data('r16'))
>>> image2 = ants.abp_n4(image)

n3_bias_field_correction(image, downsample_factor=3)[source]

N3 Bias Field Correction

ANTsR function: n3BiasFieldCorrection

Parameters:

image (ants.core.ANTsImage) – image to be bias corrected
downsample_factor (scalar) – how much to downsample image before performing bias correction

Return type:

ants.core.ANTsImage

Example

>>> import ants
>>> image = ants.image_read( ants.get_ants_data('r16') )
>>> image_n3 = ants.n3_bias_field_correction(image)

n3_bias_field_correction2(image, mask=None, rescale_intensities=False, shrink_factor=4, convergence={'iters': 50, 'tol': 1e-07}, spline_param=None, number_of_fitting_levels=4, return_bias_field=False, verbose=False, weight_mask=None)[source]

N3 Bias Field Correction

ANTsR function: n3BiasFieldCorrection2

Parameters:

image (ants.core.ANTsImage) – image to bias correct
mask (ants.core.ANTsImage) – Input mask. If not specified, the entire image is used.
rescale_intensities (bool) – At each iteration, a new intensity mapping is calculated and applied but there is nothing which constrains the new intensity range to be within certain values. The result is that the range can “drift” from the original at each iteration. This option rescales to the [min,max] range of the original image intensities within the user-specified mask. A mask is required to perform rescaling. Default is False in ANTsR/ANTsPy but True in ANTs.
shrink_factor (scalar) – Shrink factor for multi-resolution correction, typically integer less than 4
convergence (dict w/ keys `iters` and tol) – iters : maximum number of iterations tol : the convergence tolerance. Default tolerance is 1e-7 in ANTsR/ANTsPy but 0.0 in ANTs.
spline_param (float or vector Parameter controlling number of control) – points in spline. Either single value, indicating the spacing in each direction, or vector with one entry per dimension of image, indicating the mesh size. If None, defaults to mesh size of 1 in all dimensions.
number_of_fitting_levels (int) – Number of fitting levels per iteration.
return_bias_field (bool) – Return bias field instead of bias corrected image.
verbose (bool) – enables verbose output.
weight_mask (ANTsImage (optional)) – antsImage of weight mask

Return type:

ants.core.ANTsImage

Example

>>> image = ants.image_read( ants.get_ants_data('r16') )
>>> image_n3 = ants.n3_bias_field_correction2(image)

n4_bias_field_correction(image, mask=None, rescale_intensities=False, shrink_factor=4, convergence={'iters': [50, 50, 50, 50], 'tol': 1e-07}, spline_param=None, return_bias_field=False, verbose=False, weight_mask=None)[source]

N4 Bias Field Correction

ANTsR function: n4BiasFieldCorrection

Parameters:

image (ants.core.ANTsImage) – image to bias correct
mask (ants.core.ANTsImage) – Input mask. If not specified, the entire image is used.
rescale_intensities (bool) – At each iteration, a new intensity mapping is calculated and applied but there is nothing which constrains the new intensity range to be within certain values. The result is that the range can “drift” from the original at each iteration. This option rescales to the [min,max] range of the original image intensities within the user-specified mask. A mask is required to perform rescaling. Default is False in ANTsR/ANTsPy but True in ANTs.
shrink_factor (scalar) – Shrink factor for multi-resolution correction, typically integer less than 4
convergence (dict w/ keys `iters` and tol) – iters : vector of maximum number of iterations for each level tol : the convergence tolerance. Default tolerance is 1e-7 in ANTsR/ANTsPy but 0.0 in ANTs.
spline_param (float or vector) – Parameter controlling number of control points in spline. Either single value, indicating the spacing in each direction, or vector with one entry per dimension of image, indicating the mesh size. If None, defaults to mesh size of 1 in all dimensions.
return_bias_field (bool) – Return bias field instead of bias corrected image.
verbose (bool) – enables verbose output.
weight_mask (ANTsImage (optional)) – antsImage of weight mask

Return type:

ants.core.ANTsImage

Example

>>> image = ants.image_read( ants.get_ants_data('r16') )
>>> image_n4 = ants.n4_bias_field_correction(image)