intensity-normalization
importedsoftware/intensity-normalization
Normalize MR image intensities in Python
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- active
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Documentation
- unknown
- Tags
- fcm · harmonization · intensity-normalization · mri · neuroimaging · normalization · standardization · whitestripe
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- neuroCombatharmonization · neuroimaging · normalization
Harmonization of multi-site imaging data with ComBat (Python)
- neurocombat_sklearnharmonization · neuroimaging · normalization
Implementation of Combat harmonization method with scikit-learn compatible format
- HarmonizedMRI.github.ioharmonization · mri
Official website for HarmonizedMRI—a platform dedicated to sharing MRI harmonization projects and resources.
- preprocessingmri · normalization
preprocessing tools for multi-modal 3D brain imaging
- benchmarking-toolsstandardization
Repository for the GA4GH Benchmarking Team work developing standardized benchmarking methods for germline small variant calls
- Omegastandardization
Open Microscopy Environment inteGrated Analysis (OMEGA) is a cross-platform data management, analysis and visualization system, for particle tracking data with particular emphasis on results from…
- api.github.com/repos/jcreinhold/intensity-normalizationretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-08-04, 340 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/42.json→ .entries["intensity-normalization"]
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