itk-filtering
ITK is an open-source toolkit for multidimensional image analysis
Decision gist · record as of 2026-08-14
Yes. itk-filtering is actively maintained, permissively licensed, has no known vulnerabilities, and provides stable access to ITK's filtering algorithms with minimal dependencies. Install it if you need ITK filtering operations in Python; the binary wheels reduce friction. The only caveat is verifying whether your workflow requires the full itk package or whether itk-filtering alone suffices for your use case.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; binary wheels are available for Linux (x86_64, aarch64), macOS (x86_64, arm64), and Windows (x86_64).
- Medium install friction due to binary wheel distribution across multiple platforms and Python versions.
- The package is actively maintained with a release 7 days old and depends only on itk-numerics, keeping the dependency chain minimal.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache 2.0, a permissive license allowing both commercial and non-commercial use with minimal restrictions. You may use, modify, and redistribute the package freely provided you include license and attribution notices.
last release 2026-08-07 (7 days) · last repo commit 2026-08-14 · 1,643 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 233,104 downloads/mo, #9,047 on PyPI
Alternatives
Verify before relying
pip install itk-filtering
import itk
# Load and filter an image
image = itk.imread('input.mha')
filtered = itk.median_image_filter(image, radius=2)
itk.imwrite(filtered, 'output.mha')- Whether itk-filtering is installable as a standalone package or requires the full itk metapackage for practical use.
- Performance characteristics and memory overhead for large N-dimensional image datasets.
- Specific filtering algorithms and image types supported beyond the general description.
What it is and what it does
itk-filtering is a Python module that wraps ITK's filtering algorithms for N-dimensional scientific image processing. It is part of the Insight Toolkit ecosystem and provides access to image filtering operations commonly used in medical imaging workflows, such as smoothing, edge detection, and morphological operations. The package is distributed as pre-built binary wheels, making installation straightforward across Windows, macOS, and Linux platforms.
The module depends on itk-numerics for numerical operations and is designed for developers and researchers working with medical images from CT, MRI, and other imaging modalities. It integrates with ITK's broader image processing pipeline, allowing you to chain filtering operations with segmentation and registration tasks. The toolkit supports modern Python versions (3.8+) and is actively maintained by the Insight Software Consortium, a non-profit entity sponsored by NumFOCUS.
Use it for
- Preprocessing medical images (CT/MRI scans) with noise reduction filters before segmentation or registration.
- Applying morphological operations (erosion, dilation) to binary or labeled images in medical image analysis workflows.
- Implementing edge detection and gradient-based filters for feature extraction in scientific imaging applications.
- Batch processing of multidimensional image datasets with consistent filtering pipelines across research studies.
- Building image analysis tools that require ITK's filtering capabilities without installing the full toolkit.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
itk-filtering is actively maintained, permissively licensed, has no known vulnerabilities, and provides stable access to ITK's filtering algorithms with minimal dependencies. Install it if you need ITK filtering operations in Python; the binary wheels reduce friction. The only caveat is verifying whether your workflow requires the full itk package or whether itk-filtering alone suffices for your use case.
Install
itk-filtering on PyPI
Before you install
Medium install friction due to binary wheel distribution across multiple platforms and Python versions. The package is actively maintained with a release 7 days old and depends only on itk-numerics, keeping the dependency chain minimal.
Requires Python 3.8 or later; binary wheels are available for Linux (x86_64, aarch64), macOS (x86_64, arm64), and Windows (x86_64).
License in practice
Licensed under Apache 2.0, a permissive license allowing both commercial and non-commercial use with minimal restrictions. You may use, modify, and redistribute the package freely provided you include license and attribution notices.
Quickstart
pip install itk-filtering
import itk
# Load and filter an image
image = itk.imread('input.mha')
filtered = itk.median_image_filter(image, radius=2)
itk.imwrite(filtered, 'output.mha')Verify before relying
- Whether itk-filtering is installable as a standalone package or requires the full itk metapackage for practical use.
- Performance characteristics and memory overhead for large N-dimensional image datasets.
- Specific filtering algorithms and image types supported beyond the general description.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packageitk-numerics |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 233,104 / month, #9,047 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: AndroidOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: C++Programming Language :: PythonProgramming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Medical Science Apps.Topic :: Software Development :: Libraries |
Evidence: itk_filtering-5.4.7-cp310-cp310-macosx_10_9_x86_64.whl; itk_filtering-5.4.7-cp310-cp310-macosx_11_0_arm64.whl; itk_filtering-5.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; itk_filtering-5.4.7-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; itk_filtering-5.4.7-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; itk_filtering-5.4.7-cp310-cp310-win_amd64.whl; itk_filtering-5.4.7-cp311-abi3-macosx_10_9_x86_64.whl; itk_filtering-5.4.7-cp311-abi3-macosx_11_0_arm64.whl; itk_filtering-5.4.7-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; itk_filtering-5.4.7-cp311-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; itk_filtering-5.4.7-cp311-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; itk_filtering-5.4.7-cp311-abi3-win_amd64.whl; itk_filtering-5.4.7-cp39-cp39-macosx_10_9_x86_64.whl; itk_filtering-5.4.7-cp39-cp39-macosx_11_0_arm64.whl; itk_filtering-5.4.7-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; itk_filtering-5.4.7-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; itk_filtering-5.4.7-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; itk_filtering-5.4.7-cp39-cp39-win_amd64.whl
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See also itk · itk-core · itk-segmentation · itk-numerics · itkwasm · itk-registration · simpleitk · itk-io · vtk · TotalSegmentator