"""WhiteStripe (normal-appearing white matter mean & standard deviation) normalization."""
from __future__ import annotations
__all__ = ["WhiteStripeNormalize"]
import argparse
import typing
import numpy as np
import numpy.typing as npt
import intensity_normalization.normalize.base as intnormb
import intensity_normalization.typing as intnormt
import intensity_normalization.util.histogram_tools as intnormhisttool
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class WhiteStripeNormalize(
intnormb.LocationScaleCLIMixin, intnormb.SingleImageNormalizeCLI
):
def __init__(
self,
*,
norm_value: float = 1.0,
width: float = 0.05,
width_l: float | None = None,
width_u: float | None = None,
**kwargs: typing.Any,
):
"""
Find the normal-appearing white matter of the input MR image and
use those values to standardize the data (i.e., subtract the mean of
the values in the indices and divide by the std of those values).
See the original paper for details on width.
"""
super().__init__(norm_value=norm_value, **kwargs)
self.width_l = width_l or width
self.width_u = width_u or width
self.whitestripe: npt.NDArray | None = None
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def calculate_location(
self,
image: intnormt.ImageLike,
/,
mask: intnormt.ImageLike | None = None,
*,
modality: intnormt.Modality = intnormt.Modality.T1,
) -> float:
loc: float = image[self.whitestripe].mean()
return loc
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def calculate_scale(
self,
image: intnormt.ImageLike,
/,
mask: intnormt.ImageLike | None = None,
*,
modality: intnormt.Modality = intnormt.Modality.T1,
) -> float:
scale: float = image[self.whitestripe].std()
return scale
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def setup(
self,
image: intnormt.ImageLike,
/,
mask: intnormt.ImageLike | None = None,
*,
modality: intnormt.Modality = intnormt.Modality.T1,
) -> None:
if modality is None:
modality = "t1"
mask = self._get_mask(image, mask, modality=modality)
masked = image * mask
voi = image[mask]
wm_mode = intnormhisttool.get_tissue_mode(voi, modality=modality)
wm_mode_quantile: float = np.mean(voi < wm_mode).item()
lower_bound = max(wm_mode_quantile - self.width_l, 0.0)
upper_bound = min(wm_mode_quantile + self.width_u, 1.0)
ws_l: float
ws_u: float
ws_l, ws_u = np.quantile(voi, (lower_bound, upper_bound))
self.whitestripe = (masked > ws_l) & (masked < ws_u)
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def teardown(self) -> None:
del self.whitestripe
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@staticmethod
def name() -> str:
return "ws"
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@staticmethod
def fullname() -> str:
return "WhiteStripe"
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@staticmethod
def description() -> str:
return "Standardize the normal appearing WM of a MR image."
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@staticmethod
def add_method_specific_arguments(
parent_parser: argparse.ArgumentParser,
) -> argparse.ArgumentParser:
parser = parent_parser.add_argument_group("method-specific arguments")
parser.add_argument(
"--width",
default=0.05,
type=float,
help="Width of the 'white stripe'. (See original paper.)",
)
return parent_parser
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@classmethod
def from_argparse_args(cls, args: argparse.Namespace, /) -> WhiteStripeNormalize:
return cls(width=args.width)
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def plot_histogram_from_args(
self,
args: argparse.Namespace,
/,
normalized: intnormt.ImageLike,
mask: intnormt.ImageLike | None = None,
) -> None:
if mask is None:
mask = self.estimate_foreground(normalized)
super().plot_histogram_from_args(args, normalized, mask)