Cited by 4,731Advanced MRI Techniques and ApplicationsMedical Image Segmentation TechniquesBrain Tumor Detection and Classification

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

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)
Venue
IEEE Transactions on Medical Imaging
Published
1998
Volume
17
Issue
1
Pages
87-97
ISSN
0278-0062 (ISSN-L)
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
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
  • Life Sciences › Neuroscience › Neurology › 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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