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Intensity Normalization\n\nA modern Python package for normalizing MR image intensities.\n\n## Features\n\n- **πŸ”§ Multiple Image Format Support**: Works with numpy arrays and nibabel images (.nii, .nii.gz, .mgz, .mnc, etc.)\n- **πŸ“Š 6 Normalization Methods**: FCM, KDE, WhiteStripe, Z-score, NyΓΊl, LSQ\n- **⚑ High Performance**: Optimized implementations\n\n## Installation\n\n```bash\npip install intensity-normalization\n```\nOr via conda:\n```sh\nconda install conda-forge::intensity-normalization\n```\n\n## Quick Start\n\n### High-Level API\n\n```python\nimport numpy as np\nimport nibabel as nib\nfrom intensity_normalization import normalize_image\n\n# Load MRI image and brain mask\nimg = nib.load(\"brain_t1.nii.gz\")\nmask = nib.load(\"brain_mask.nii.gz\")\n\n# Normalize using FCM (default, recommended for T1)\nnormalized = normalize_image(img, method=\"fcm\", mask=mask)\n\n# Different methods for different modalities\nt2_normalized = normalize_image(img, method=\"kde\", modality=\"t2\")\nzscore_normalized = normalize_image(img, method=\"zscore\")\n```\n\n### Object-Oriented API\n\n```python\nfrom intensity_normalization import FCMNormalizer, ZScoreNormalizer\nimport numpy as np\n\n# Create synthetic brain data\nbrain_data = np.random.normal(1000, 200, (64, 64, 32)) # WM ~1000\nbrain_data[20:40, 20:40, 10:20] = np.random.normal(600, 100, (20, 20, 10)) # GM ~600\n\n# FCM normalization (tissue-based)\nfcm = FCMNormalizer(tissue_type=\"wm\") # white matter reference\nnormalized = fcm.fit_transform(brain_data)\n\n# Z-score normalization\nzscore = ZScoreNormalizer()\nstandardized = zscore.fit_transform(brain_data)\n\nprint(f\"Original mean: {brain_data[brain_data \u003e 0].mean():.1f}\")\nprint(f\"FCM normalized WM mean: {normalized[40:60, 40:60, 10:20].mean():.2f}\")\nprint(f\"Z-score mean: {standardized[brain_data \u003e 0].mean():.2f}\")\n```\n\n### Population-Based Methods\n\n```python\nfrom intensity_normalization import NyulNormalizer, LSQNormalizer\nfrom intensity_normalization.adapters import create_image\n\n# Load multiple subjects\nimage_paths = [\"subject1_t1.nii.gz\", \"subject2_t1.nii.gz\", \"subject3_t1.nii.gz\"]\nimages = [create_image(path) for path in image_paths]\n\n# NyΓΊl histogram matching\nnyul = NyulNormalizer(output_min_value=0, output_max_value=100)\nnyul.fit_population(images)\n\n# Normalize all images to the same scale\nnormalized_images = [nyul.transform(img) for img in images]\n\n# LSQ tissue mean harmonization\nlsq = LSQNormalizer()\nlsq.fit_population(images)\nharmonized_images = [lsq.transform(img) for img in images]\n```\n\n## Command Line Interface\n\n### Single Image Normalization\n\n```bash\n# Basic usage\nintensity-normalize fcm brain_t1.nii.gz\n\n# With brain mask\nintensity-normalize fcm brain_t1.nii.gz -m brain_mask.nii.gz\n\n# Specify output location\nintensity-normalize zscore brain_t1.nii.gz -o normalized_brain.nii.gz\n\n# Different modalities and tissue types\nintensity-normalize kde brain_t2.nii.gz --modality t2 --tissue-type gm\nintensity-normalize whitestripe brain_flair.nii.gz --modality flair --width 0.1\n```\n\n### Method-Specific Parameters\n\n```bash\n# FCM with different tissue types and clusters\nintensity-normalize fcm brain.nii.gz --tissue-type wm --n-clusters 3\n\n# WhiteStripe with custom width\nintensity-normalize whitestripe brain.nii.gz --width 0.05\n\n# Get help for specific methods\nintensity-normalize fcm --help\n```\n\n## Supported File Formats\n\nWorks with all neuroimaging formats supported by nibabel:\n\n| Format | Extensions | Description |\n|--------|------------|-------------|\n| **NIfTI** | `.nii`, `.nii.gz` | Most common neuroimaging format |\n| **FreeSurfer** | `.mgz`, `.mgh` | FreeSurfer volume format |\n| **ANALYZE** | `.hdr/.img` | Legacy format pair |\n| **MINC** | `.mnc` | Medical Imaging NetCDF |\n| **PAR/REC** | `.par/.rec` | Philips scanner format |\n| **Numpy** | `.npy` | Raw numpy arrays |\n\n## Normalization Methods\n\n### Individual Methods (Single Image)\n\n| Method | Best For | Description |\n|--------|----------|-------------|\n| **FCM** | T1-weighted | Fuzzy C-means tissue segmentation (recommended) |\n| **Z-Score** | Any modality | Standard score normalization |\n| **KDE** | T1/T2/FLAIR | Kernel density estimation of tissue modes |\n| **WhiteStripe** | T1-weighted | Normal-appearing white matter standardization |\n\n### Population Methods (Multiple Images)\n\n| Method | Best For | Description |\n|--------|----------|-------------|\n| **NyΓΊl** | Cross-scanner | Piecewise linear histogram matching |\n| **LSQ** | Multi-site studies | Least squares tissue mean harmonization |\n\n### Method Selection Guide\n\n```python\n# T1-weighted images (structural)\nnormalize_image(t1_image, method=\"fcm\", tissue_type=\"wm\")\n\n# T2-weighted or FLAIR\nnormalize_image(t2_image, method=\"kde\", modality=\"t2\")\n\n# Quick standardization\nnormalize_image(image, method=\"zscore\")\n\n# Multi-site harmonization (requires multiple subjects)\nfrom intensity_normalization.services.normalization import NormalizationService\nconfig = NormalizationConfig(method=\"nyul\")\nharmonized = NormalizationService.normalize_images(all_images, config)\n```\n\n## Architecture Overview\n\nThe package is structured as follows:\n\n```\nintensity_normalization/\nβ”œβ”€β”€ domain/ # Core logic\nβ”‚ β”œβ”€β”€ protocols.py # Image and normalizer interfaces\nβ”‚ β”œβ”€β”€ models.py # Configuration and value objects\nβ”‚ └── exceptions.py# Domain-specific exceptions\nβ”œβ”€β”€ adapters/ # External interfaces\nβ”‚ β”œβ”€β”€ images.py # Universal image adapter (numpy/nibabel)\nβ”‚ └── io.py # File I/O operations\nβ”œβ”€β”€ normalizers/ # Normalization implementations\nβ”‚ β”œβ”€β”€ individual/ # Single-image methods (FCM, Z-score, etc.)\nβ”‚ └── population/ # Multi-image methods (NyΓΊl, LSQ)\nβ”œβ”€β”€ services/ # Application services\nβ”‚ β”œβ”€β”€ normalization.py # Orchestration logic\nβ”‚ └── validation.py # Input validation\n└── cli.py # Command-line interface\n```\n\n## Advanced Usage\n\n### Custom Normalizers\n\n```python\nfrom intensity_normalization.domain.protocols import BaseNormalizer\nfrom intensity_normalization.domain.protocols import ImageProtocol\n\nclass CustomNormalizer(BaseNormalizer):\n def fit(self, image: ImageProtocol, mask=None) -\u003e 'CustomNormalizer':\n # Implement fitting logic\n self.is_fitted = True\n return self\n\n def transform(self, image: ImageProtocol, mask=None) -\u003e ImageProtocol:\n # Implement normalization logic\n data = image.get_data()\n normalized_data = your_normalization_function(data)\n return image.with_data(normalized_data)\n```\n\n### Configuration-Based Workflow\n\n```python\nfrom intensity_normalization.domain.models import NormalizationConfig, Modality, TissueType\nfrom intensity_normalization.services.normalization import NormalizationService\n\n# Create configuration\nconfig = NormalizationConfig(\n method=\"fcm\",\n modality=Modality.T1,\n tissue_type=TissueType.WM\n)\n\n# Validate configuration\nfrom intensity_normalization.services import ValidationService\nValidationService.validate_normalization_config(config)\n\n# Apply normalization\nresult = NormalizationService.normalize_image(image, config, mask)\n```\n\n### Batch Processing\n\n```python\nfrom pathlib import Path\nfrom intensity_normalization.adapters.images import create_image, save_image\nfrom intensity_normalization.services.normalization import NormalizationService\n\ndef process_directory(input_dir: Path, output_dir: Path, method: str = \"fcm\"):\n \"\"\"Process all NIfTI files in a directory.\"\"\"\n output_dir.mkdir(exist_ok=True)\n\n for img_file in input_dir.glob(\"*.nii.gz\"):\n # Load image\n image = create_image(img_file)\n\n # Create configuration\n config = NormalizationConfig(method=method)\n\n # Normalize\n normalized = NormalizationService.normalize_image(image, config)\n\n # Save result\n output_file = output_dir / f\"{img_file.stem}_normalized.nii.gz\"\n save_image(normalized, output_file)\n print(f\"Processed: {img_file.name}\")\n\n# Usage\nprocess_directory(Path(\"raw_images/\"), Path(\"normalized/\"))\n```\n\n## Development\n\n### Setup Development Environment\n\n```bash\n# Clone and setup\ngit clone https://github.com/jcreinhold/intensity-normalization.git\ncd intensity-normalization\n\n# Install uv (modern Python package manager)\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n\n# Create virtual environment and install dependencies\nuv sync --dev\n```\n\n### Code Quality\n\n```bash\n# Format code\nuv run ruff format intensity_normalization/\n\n# Lint code\nuv run ruff check intensity_normalization/\n\n# Type checking\nuv run mypy intensity_normalization/\n\n# Run tests\nuv run pytest\n\n# Run tests with coverage\nuv run pytest --cov=intensity_normalization --cov-report=html\n```\n\n### Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Make changes and add tests\n4. Ensure code quality (`uv run ruff format \u0026\u0026 uv run ruff check --fix \u0026\u0026 uv run mypy`)\n5. Run tests (`uv run pytest`)\n6. Commit changes (`git commit -m 'Add amazing feature'`)\n7. Push to branch (`git push origin feature/amazing-feature`)\n8. Open a Pull Request\n\n## Citation\n\nIf you use this package in your research, please cite:\n\n```bibtex\n@inproceedings{reinhold2019evaluating,\n title={Evaluating the impact of intensity normalization on {MR} image synthesis},\n author={Reinhold, Jacob C and Dewey, Blake E and Carass, Aaron and Prince, Jerry L},\n booktitle={Medical Imaging 2019: Image Processing},\n volume={10949},\n pages={109493H},\n year={2019},\n organization={International Society for Optics and Photonics}}\n```\n\n## Related Papers\n\n- **FCM**: Udupa, J.K., et al. \"A framework for evaluating image segmentation algorithms.\" Computerized medical imaging and graphics 30.2 (2006): 75-87.\n- **NyΓΊl**: NyΓΊl, L.G., Udupa, J.K. \"On standardizing the MR image intensity scale.\" Magnetic Resonance in Medicine 42.6 (1999): 1072-1081.\n- **WhiteStripe**: Shinohara, R.T., et al. \"Statistical normalization techniques for magnetic resonance imaging.\" NeuroImage 132 (2016): 174-184.\n\n## Support\n\n- **Issues**: [GitHub Issues](https://github.com/jcreinhold/intensity-normalization/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/jcreinhold/intensity-normalization/discussions)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcreinhold%2Fintensity-normalization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcreinhold%2Fintensity-normalization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcreinhold%2Fintensity-normalization/lists"}