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intensity-normalization

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Normalize MR image intensities in Python

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최종 버전 다운로드 (.zip)

- - bug-report.md
  - config.yml
  - feature-request.md
- - docs.yml
  - publish.yml
  - tests.yml
- settings.json
- - intnorm_illustration.png
- - mathjax.js
- algorithms.md
- api.md
- cli.md
- how-to.md
- index.md
- methods.md
- migration.md
- quickstart.md
- - __init__.py
  - _ants.py
  - _image.py
  - errors.py
  - histogram.py
  - io.py
  - py.typed
- conftest.py
- phantoms.py
- test_cli.py
- test_individual.py
- test_laws.py
- test_metadata.py
- test_population.py
- test_ravel.py
- test_regressions.py
- test_tools.py
.gitignore
.mdwright.toml
AGENTS.md
CHANGELOG.md
CLAUDE.md
LICENSE
mkdocs.yml
pyproject.toml
README.md
uv.lock

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# intensity-normalization

[![PyPI](https://img.shields.io/pypi/v/intensity-normalization.svg)](https://pypi.org/project/intensity-normalization/)
[![Docs](https://img.shields.io/badge/docs-github%20pages-blue)](https://jcreinhold.github.io/intensity-normalization/)

Normalize the intensities of magnetic resonance (MR) images — T1-w, T2-w, FLAIR, PD-w — across scanners, sites, and
sessions.

MR images have no consistent intensity scale; the inconsistency is an acquisition artifact that breaks downstream
processing (especially ML). This package implements the standard fixes:

- **Individual methods** — `zscore`, `fcm`, `kde`, `whitestripe`: plain functions of one image.
- **Population methods** — `nyul`, `lsq`, `ravel`: learn a transform from a set of images; save it, apply it to new
  scans.
- **Tools** — tissue membership maps, histogram plotting (validation), N4 preprocessing, co-registration (ANTs).

## Install

```bash
pip install intensity-normalization            # or: uv add intensity-normalization
pip install "intensity-normalization[ants]"  # ravel registration, preprocess, coregister
pip install "intensity-normalization[plot]"  # histogram plotting
```

The CLI also runs without installing: `uvx intensity-normalize --help`.

## Quickstart

```python
import intensity_normalization as inorm

normed = inorm.whitestripe(t1w_image, mask=brain_mask)  # numpy or nibabel in → same type out

tx = inorm.nyul.fit(train_images, masks=train_masks)  # population: fit once...
tx.save("nyul.npz")
normed_new = tx(new_image)  # ...apply to new scans
```

```bash
intensity-normalize fcm t1w.nii.gz -m brain_mask.nii.gz -p
intensity-normalize nyul images/ -m masks/ -o normalized/ --save-state nyul.npz
```

**[Documentation](https://jcreinhold.github.io/intensity-normalization/)** —
[quickstart](https://jcreinhold.github.io/intensity-normalization/quickstart/),
[how-to guides](https://jcreinhold.github.io/intensity-normalization/how-to/),
[choosing a method](https://jcreinhold.github.io/intensity-normalization/methods/),
[algorithms](https://jcreinhold.github.io/intensity-normalization/algorithms/),
[CLI](https://jcreinhold.github.io/intensity-normalization/cli/),
[API](https://jcreinhold.github.io/intensity-normalization/api/),
[migrating to v4](https://jcreinhold.github.io/intensity-normalization/migration/).

## Reference

If you use this package, please cite the accompanying [pre-print](https://arxiv.org/abs/1812.04652):

```bibtex
@article{reinhold2019evaluating,
  title={Evaluating the impact of intensity normalization on MR image synthesis},
  author={Reinhold, Jacob C and Dewey, Blake E and Carass, Aaron and Prince, Jerry L},
  journal={Medical Imaging 2019: Image Processing},
  year={2019}
}
```

## License

MIT