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Auxiliary is a Python package providing utility functions for medical image processing. It is part of the BrainLesion project and offers tools for:
- Image I/O: Reading and writing medical images (NIfTI, TIFF, DICOM) using SimpleITK
- Image Normalization: Percentile-based and windowing normalization methods
- Format Conversion: DICOM to NIfTI and NIfTI to DICOM conversion
- Path Utilities: Robust path handling with the turbopath module
With a Python 3.10+ environment, you can install auxiliary directly from PyPI:
pip install auxiliary
Or via conda:
conda install conda-forge::auxiliary
For DICOM to NIfTI conversion using dcm2niix:
pip install auxiliary[dcm2niix]
from auxiliary.io import read_image, write_image # Read a NIfTI image image_array = read_image("path/to/image.nii.gz") # Write a NumPy array to a NIfTI file write_image(image_array, "path/to/output.nii.gz") # Write with reference image for spatial metadata write_image(image_array, "path/to/output.nii.gz", reference_path="path/to/reference.nii.gz")
from auxiliary.conversion import dicom_to_nifti_itk, nifti_to_dicom_itk, dcm2niix import numpy as np # Read a DICOM series and convert to NIfTI using SimpleITK dicom_to_nifti_itk("path/to/dicom_dir", "path/to/output_dir") # Read a DICOM series and convert to NIfTI using dcm2niix (requires dcm2niix extra) dcm2niix("path/to/dicom_dir", "path/to/output_dir") # Write a NIfTI image to DICOM format nifti_to_dicom_itk("path/to/image.nii.gz", "path/to/output_dicom_dir") # Write a NumPy array to DICOM format image_array = np.random.rand(128, 128, 64) # example 3D array nifti_to_dicom_itk(image_array, "path/to/output_dicom_dir") # Write a NumPy array to DICOM with reference DICOM for metadata nifti_to_dicom_itk( image_array, "path/to/output_dicom_dir", reference_dicom="path/to/reference_dicom_dir" )
from auxiliary.tiff.io import read_tiff, write_tiff # Read a TIFF file tiff_data = read_tiff("path/to/image.tiff") # Write a NumPy array to a TIFF file write_tiff(tiff_data, "path/to/output.tiff")
from auxiliary.normalization.percentile_normalizer import PercentileNormalizer from auxiliary.normalization.windowing_normalizer import WindowingNormalizer # Percentile-based normalization normalizer = PercentileNormalizer(lower_percentile=1.0, upper_percentile=99.0) normalized_image = normalizer.normalize(image_array) # Windowing normalization (e.g., for CT images) normalizer = WindowingNormalizer(center=40, width=400) windowed_image = normalizer.normalize(image_array)
Important
If you use auxiliary in your research, please cite it to support the development!
Kofler, F., Rosier, M., Astaraki, M., Möller, H., Mekki, I. I., Buchner, J. A., Schmick, A., Pfiffer, A., Oswald, E., Zimmer, L., Rosa, E. de la, Pati, S., Canisius, J., Piffer, A., Baid, U., Valizadeh, M., Linardos, A., Peeken, J. C., Shit, S., ... Menze, B. (2025). BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis arXiv preprint arXiv:2507.09036
@misc{kofler2025brainlesionsuiteflexibleuserfriendly,
title={BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis},
author={Florian Kofler and Marcel Rosier and Mehdi Astaraki and Hendrik Möller and Ilhem Isra Mekki and Josef A. Buchner and Anton Schmick and Arianna Pfiffer and Eva Oswald and Lucas Zimmer and Ezequiel de la Rosa and Sarthak Pati and Julian Canisius and Arianna Piffer and Ujjwal Baid and Mahyar Valizadeh and Akis Linardos and Jan C. Peeken and Surprosanna Shit and Felix Steinbauer and Daniel Rueckert and Rolf Heckemann and Spyridon Bakas and Jan Kirschke and Constantin von See and Ivan Ezhov and Marie Piraud and Benedikt Wiestler and Bjoern Menze},
year={2025},
eprint={2507.09036},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.09036},
}