The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Talairach, J., Tournoux, P., & Rayport, M. (1988) · Thieme eBooks · Cited by 5,058
Non-Uniform Random Variate Generation
Devroye, L. (1986) · Springer eBooks · doi:10.1007/978-1-4613-8643-8 · Cited by 4,090
Pattern classification and scene analysis
Chien, Y. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100577 · Cited by 3,421
Adaptive segmentation of MRI data
Wells, W. M., Grimson, W. E. L., Kikinis, R., & Jolesz, F. A. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.511747 · Cited by 1,291
Morphometric analysis of white matter lesions in MR images: method and validation
Zijdenbos, A. P., Dawant, B. M., Margolin, R. A., & Palmer, A. C. (1994) · IEEE Transactions on Medical Imaging · doi:10.1109/42.363096 · Cited by 1,268
Self-citationBloch, F., Hansen, W. W., & Packard, M. E. (1946) · Physical Review · doi:10.1103/physrev.69.127 · Cited by 945
Flagged first, then by citations
Research works printed but not linked: 2
- thulborn, 1993, b1 correction maps and apparent water density maps as tools for quantitative functional mri, Soc Magn Reson Med, 1, 347
- zijdenbos, 1995, intensity correction and its effect on measurement variability in the computer-aided analysis of mri, Proc 9th Int Symp Exhibit Computer Assisted Radiology (CAR), 216
Cited by
Sorted by citations
Items 201–216 of 216
Khan, A. F., Adewale, Q., Lin, S.-J., Baumeister, T. R., Zeighami, Y., Carbonell, F., Palomero-Gallagher, N., & Iturria-Medina, Y. (2023) · Nature Communications · doi:10.1038/s41467-023-41677-w · Cited by 28
Lee, N. R., Wallace, G. L., Raznahan, A., Clasen, L. S., & Giedd, J. N. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00496 · Cited by 30
“The native MRI scans were registered into standardized stereotaxic space and were corrected for nonuniformity artifacts (Sled et al., 1998) using a linear transformation (Collins et al., 1994).”
MethodIn vivo high-resolution 7 Tesla MRI shows early and diffuse cortical alterations in CADASIL
De Guio, F., Reyes, S., Vignaud, A., Duering, M., Ropele, S., Duchesnay, E., Chabriat, H., & Jouvent, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106311 · Cited by 28
Whiteside, S. P. H., Abramowitz, J. S., & Port, J. D. (2012) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2011.11.010 · Cited by 27
Blackmon, K., Ben-Avi, E., Wang, X., Pardoe, H. R., Di Martino, A., Halgren, E., Devinsky, O., Thesen, T., & Kuzniecky, R. (2016) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.10.017 · Cited by 27
Zierhut, K. C., Schulte-Kemna, A., Kaufmann, J., Steiner, J., Bogerts, B., & Schiltz, K. (2013) · Cortex · doi:10.1016/j.cortex.2012.08.027 · Cited by 26
Wang, Z., Dai, Z., Shu, H., Liu, D., Guo, Q., He, Y., & Zhang, Z. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-160724 · Cited by 26
MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia
Manera, A. L., Dadar, M., Van Swieten, J. C., Borroni, B., Sanchez-Valle, R., Moreno, F., Laforce Jr, R., Graff, C., Synofzik, M., Galimberti, D., et al. (2021) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2020-324106 · Cited by 23
Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences
Kushki, A., Cardy, R. E., Panahandeh, S., Malihi, M., Hammill, C., Brian, J., Iaboni, A., Taylor, M. J., Schachar, R., Crosbie, J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab142 · Cited by 25
MacDonald, M. E., Williams, R. J., Rajashekar, D., Stafford, R. B., Hanganu, A., Sun, H., Berman, A. J. L., McCreary, C. R., Frayne, R., Forkert, N. D., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.019 · Cited by 21
MUKHTAR, G., & FARHAN, S. (2020) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2020.02013 · Cited by 19
Katsuki, A., Watanabe, K., Nguyen, L., Otsuka, Y., Igata, R., Ikenouchi, A., Kakeda, S., Korogi, Y., & Yoshimura, R. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21093032 · Cited by 15
Cárdenas-de-la-Parra, A., Martin-Brevet, S., Moreau, C., Rodriguez-Herreros, B., Fonov, V. S., Maillard, A. M., Zürcher, N. R., Addor, M.-C., Andrieux, J., Arveiler, B., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116155 · Cited by 14
Neuroanatomical correlates of perceived stress controllability in adolescents and emerging adults
Fassett-Carman, A. N., Smolker, H., Hankin, B. L., Snyder, H. R., & Banich, M. T. (2022) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-022-00985-2 · Cited by 14
Bompard, L., Xu, S., Styner, M., Paniagua, B., Ahn, M., Yuan, Y., Jewells, V., Gao, W., Shen, D., Zhu, H., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108306 · Cited by 11
“The images were pre-processed, including removal of non-brain tissues such as the skull and dura using Brain Surface Extractor (BSE) [25], bias correction using the non-parametric non-uniform intensity normalization (N3) method [26], and resampling to a resolution of 16161 mm(3).”
MethodLiang, Z., Guo, X., Xu, W., Ibrahim, Y., Voets, N., Pretorius, P. M., Noble, J. A., & Kamnitsas, K. (2026) · Medical Image Analysis · doi:10.1016/j.media.2025.103763 · Cited by 3
Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
