A nonparametric method for automatic correction of intensity nonuniformity in MRI data
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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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The correction for image intensity has been proven to recover most of the image artifacts present in the ICBM MR database.
“Also, the correction for image intensity has been proven to recover most of the image artifacts present in the International Consortium of Brain Mapping MR database (Sled et al., 1998).”
From Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood (2005) · cites this paper as Sled et al., 1998
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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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