intensity_normalization.normalize package¶
Submodules¶
intensity_normalization.normalize.base module¶
Base class for normalization methods Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: 01 Jun 2021
- class intensity_normalization.normalize.base.DirectoryNormalizeCLI[source]¶
Bases:
SampleNormalizeCLIMixin
,DirectoryCLI
- before_fit(images: collections.abc.Sequence[intnormt.ImageLike], /, masks: collections.abc.Sequence[intnormt.ImageLike] | None = None, *, modality: intnormt.Modality = Modality.T1, **kwargs: Any) tuple[ImageSeq, MaskSeqOrNone] [source]¶
- class intensity_normalization.normalize.base.LocationScaleCLIMixin(*, norm_value: float = 1.0, **kwargs: Any)[source]¶
Bases:
LocationScaleMixin
,NormalizeCLIMixin
- class intensity_normalization.normalize.base.SingleImageNormalizeCLI[source]¶
Bases:
NormalizeCLIMixin
,SingleImageCLI
intensity_normalization.normalize.fcm module¶
Fuzzy C-Means-based tissue mean normalization Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: 01 Jun 2021
- class intensity_normalization.normalize.fcm.FCMNormalize(*, norm_value: float = 1.0, tissue_type: TissueType = TissueType.WM, **kwargs: Any)[source]¶
Bases:
LocationScaleCLIMixin
,SingleImageNormalizeCLI
- calculate_location(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- calculate_scale(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- classmethod from_argparse_args(args: Namespace, /) FCMNormalize [source]¶
- classmethod get_parent_parser(desc: str, valid_modalities: frozenset[str] = frozenset({'flair', 'md', 'other', 'pd', 't1', 't2'}), **kwargs: Any) ArgumentParser [source]¶
- property is_fit: bool¶
intensity_normalization.normalize.kde module¶
Kernel density estimation-based tissue mode normalization Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: 01 Jun 2021
- class intensity_normalization.normalize.kde.KDENormalize(norm_value: float = 1.0, **kwargs: Any)[source]¶
Bases:
LocationScaleCLIMixin
,SingleImageNormalizeCLI
- calculate_location(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
intensity_normalization.normalize.lsq module¶
Least-squares fit tissue means of a set of images Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: 01 Jun 2021
- class intensity_normalization.normalize.lsq.LeastSquaresNormalize(*, norm_value: float = 1.0, **kwargs: Any)[source]¶
Bases:
LocationScaleCLIMixin
,DirectoryNormalizeCLI
- calculate_location(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- calculate_scale(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- classmethod from_argparse_args(args: Namespace, /) LeastSquaresNormalize [source]¶
intensity_normalization.normalize.nyul module¶
Nyul & Udupa piecewise linear histogram matching normalization Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: 02 Jun 2021
- class intensity_normalization.normalize.nyul.NyulNormalize(*, output_min_value: float = 1.0, output_max_value: float = 100.0, min_percentile: float = 1.0, max_percentile: float = 99.0, percentile_after_min: float = 10.0, percentile_before_max: float = 90.0, percentile_step: float = 10.0)[source]¶
Bases:
DirectoryNormalizeCLI
- classmethod from_argparse_args(args: Namespace, /) NyulNormalize [source]¶
- normalize_image(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) intnormt.ImageLike [source]¶
- property percentiles: ndarray[Any, dtype[ScalarType]]¶
intensity_normalization.normalize.ravel module¶
RAVEL normalization (WhiteStripe then CSF correction) Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: Jun 02, 2021
- class intensity_normalization.normalize.ravel.RavelNormalize(*, membership_threshold: float = 0.99, register: bool = True, num_unwanted_factors: int = 1, sparse_svd: bool = False, whitestripe_kwargs: dict[str, Any] | None = None, quantile_to_label_csf: float = 1.0, masks_are_csf: bool = False)[source]¶
Bases:
DirectoryNormalizeCLI
- create_image_matrix_and_control_voxels(images: collections.abc.Sequence[intnormt.ImageLike], /, masks: collections.abc.Sequence[intnormt.ImageLike] | None = None, *, modality: intnormt.Modality = Modality.T1) tuple[npt.NDArray, npt.NDArray] [source]¶
creates a matrix of images; rows correspond to voxels, columns are images
- Parameters:
images – list of MR images of interest
masks – list of corresponding brain masks
modality – modality of the set of images (e.g., t1)
- Returns:
rows are voxels, columns are images control_voxels: rows are csf intersection voxels, columns are images
- Return type:
image_matrix
- estimate_unwanted_factors(control_voxels: ndarray[Any, dtype[ScalarType]]) ndarray[Any, dtype[ScalarType]] [source]¶
- classmethod from_argparse_args(args: Namespace, /) RavelNormalize [source]¶
- normalize_image(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) intnormt.ImageLike [source]¶
- process_directories(image_dir: intnormt.PathLike, /, mask_dir: intnormt.PathLike | None = None, *, modality: intnormt.Modality = Modality.T1, ext: str = 'nii*', return_normalized_and_masks: bool = False, **kwargs: Any) tuple[list[mioi.Image], list[mioi.Image] | None] | None [source]¶
- property template: ants.ANTsImage | None¶
- property template_mask: ants.ANTsImage | None¶
intensity_normalization.normalize.whitestripe module¶
WhiteStripe (normal-appearing white matter mean & standard deviation) normalization Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: 01 Jun 2021
- class intensity_normalization.normalize.whitestripe.WhiteStripeNormalize(*, norm_value: float = 1.0, width: float = 0.05, width_l: float | None = None, width_u: float | None = None, **kwargs: Any)[source]¶
Bases:
LocationScaleCLIMixin
,SingleImageNormalizeCLI
- calculate_location(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- calculate_scale(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- classmethod from_argparse_args(args: Namespace, /) WhiteStripeNormalize [source]¶
- plot_histogram_from_args(args: argparse.Namespace, /, normalized: intnormt.ImageLike, mask: intnormt.ImageLike | None = None) None [source]¶
intensity_normalization.normalize.zscore module¶
Z-score normalize image (voxel-wise subtract mean, divide by standard deviation) Author: Jacob Reinhold <jcreinhold@gmail.com> Created on: 01 Jun 2021
- class intensity_normalization.normalize.zscore.ZScoreNormalize(*, norm_value: float = 1.0, **kwargs: Any)[source]¶
Bases:
LocationScaleCLIMixin
,SingleImageNormalizeCLI
- calculate_location(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- calculate_scale(image: intnormt.ImageLike, /, mask: intnormt.ImageLike | None = None, *, modality: intnormt.Modality = Modality.T1) float [source]¶
- plot_histogram_from_args(args: argparse.Namespace, /, normalized: intnormt.ImageLike, mask: intnormt.ImageLike | None = None) None [source]¶