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RescaleIntensity

RescaleIntensity

Bases: NormalizationTransform

Rescale intensity values to a certain range.

Parameters:

Name Type Description Default
out_min_max TypeDoubleFloat

Range \((n_{min}, n_{max})\) of output intensities. If only one value \(d\) is provided, \((n_{min}, n_{max}) = (-d, d)\).

 (0, 1)
percentiles TypeDoubleFloat

Percentile values of the input image that will be mapped to \((n_{min}, n_{max})\). They can be used for contrast stretching, as in this scikit-image example. For example, Isensee et al. use (0.5, 99.5) in their nn-UNet paper. If only one value \(d\) is provided, \((n_{min}, n_{max}) = (0, d)\).

 (0, 100)
masking_method TypeMaskingMethod

See NormalizationTransform.

 None
in_min_max TypeDoubleFloat | None

Range \((m_{min}, m_{max})\) of input intensities that will be mapped to \((n_{min}, n_{max})\). If None, the minimum and maximum input intensities will be used.

 None
**kwargs

See Transform for additional keyword arguments.

 {}

Examples:

>>> import torchio as tio
>>> ct = tio.ScalarImage('ct_scan.nii.gz')
>>> ct_air, ct_bone = -1000, 1000
>>> rescale = tio.RescaleIntensity(
...     out_min_max=(-1, 1), in_min_max=(ct_air, ct_bone))
>>> ct_normalized = rescale(ct)

__call__(data) 

__call__(data: Subject) -> Subject

__call__(data: ImageT) -> ImageT

__call__(data: torch.Tensor) -> torch.Tensor

__call__(data: np.ndarray) -> np.ndarray

__call__(data: sitk.Image) -> sitk.Image

__call__(data: dict[str, object]) -> dict[str, object]

__call__(data: nib.Nifti1Image) -> nib.Nifti1Image

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data TypeTransformInput

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.

arguments_are_dict()

Check if main arguments are dict.

Return True if the type of all attributes specified in the args_names have dict type.