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)`

Transform data and return a result of the same type.

Parameters:

Name	Type	Description	Default
`data`	`InputType`	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

`get_base_args()`

Provides easy access to the arguments used to instantiate the base class (Transform) of any transform.

This method is particularly useful when a new transform can be represented as a variant of an existing transform (e.g. all random transforms), allowing for seamless instantiation of the existing transform with the same arguments as the new transform during apply_transform.

Note

The p argument (probability of applying the transform) is excluded to avoid multiplying the probability of both existing and new transform.

`add_base_args(arguments, overwrite_on_existing=False)`

Add the init args to existing arguments

`validate_keys_sequence(keys, name)` `staticmethod`

Ensure that the input is not a string but a sequence of strings.

`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.

RescaleIntensity