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A nonparametric method for automatic correction of intensity nonuniformity in MRI data

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Sled, J.G., Zijdenbos, A.P., Evans, A.C.·1998·IEEE Transactions on Medical Imaging

DOI 10.1109/42.668698

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Abstract

A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.

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Authors: Sled, J. G., Zijdenbos, A. P., & Evans, A. C.
Affiliations: Montreal Neurological Institute and Hospital (CA)
ORCID: Sled, J.G.
Evans, A.C.
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 1998
Volume: 17
Issue: 1
Pages: 87-97
ISSN: 0278-0062 (ISSN-L)
DOI: 10.1109/42.668698
OpenAlex: W2157848968
Cited by: 4,731
References: 51
Citations by year: 2026: 92 · 2025: 148 · 2024: 159 · 2023: 176 · 2022: 187 · 2021: 240 · 2020: 227 · 2019: 257 · 2018: 246 · 2017: 260 · 2016: 251 · 2015: 303 · 2014: 293 · 2013: 298 · 2012: 275
FWCI: 36.12
Type: journal article
Language: en
Open access: green
escholarship.mcgill.ca
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
Library and Archives Canada (Government of Canada) (submittedVersion)
eScholarship@McGill (McGill) (submittedVersion)
McGill-DEV
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Keywords: magnetic resonance imaging, bias field estimation, iterative estimation, N3
MeSH terms: Brain, Humans, Magnetic Resonance Imaging, Models, Theoretical

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A nonparametric method for automatic correction of intensity nonuniformity in MRI data

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