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
Anatomical mapping of functional activation in stereotactic coordinate space.
Evans, A. C., Marrett, S., Neelin, P., Collins, D. L., Worsley, K., Dai, W.-Q., Milot, S., Meyer, E., & Bub, D. (1992) · NeuroImage · doi:10.1016/1053-8119(92)90006-9 · Cited by 444
Self-citationHigh frequency volume coils for clinical NMR imaging and spectroscopy
Vaughan, J. T., Hetherington, H. P., Otu, J. O., Pan, J. W., & Pohost, G. M. (1994) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910320209 · Cited by 374
Imaging of the active B1 field in vivo
Stollberger, R., & Wach, P. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350217 · Cited by 357
Segmentation of MR Brain Images into Cerebrospinal Fluid Spaces, White and Gray Matter
Lim, K. O., & Pfefferbaum, A. (1989) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-198907000-00006 · Cited by 311
A study of methods of choosing the smoothing parameter in image restoration by regularization
Thompson, A., Brown, J. C., Kay, J., et al. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.88568 · Cited by 308
Correction of intensity variations in MR images for computer-aided tissue classification
Dawant, B. M., Zijdenbos, A. P., & Margolin, R. A. (1993) · IEEE Transactions on Medical Imaging · doi:10.1109/42.251128 · Cited by 291
Self-citationPartial volume tissue classification of multichannel magnetic resonance images-a mixel model
Choi, H. S., Haynor, D. R., & Kim, Y. (1991) · IEEE Transactions on Medical Imaging · doi:10.1109/42.97590 · Cited by 283
Sources of intensity nonuniformity in spin echo images at 1.5 T
Simmons, A., Tofts, P. S., Barker, G. J., & Arridge, S. (1994) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910320117 · Cited by 277
Comparison of linear and circular polarization for magnetic resonance imaging
Glover, G. H., Hayes, C. E., Pelc, N. J., Edelstein, W. A., Mueller, O. M., Hart, H. R., Jr., Hardy, C. J., O’Donnell, M., & Barber, W. D. (1985) · Journal of Magnetic Resonance (1969) · doi:10.1016/0022-2364(85)90349-x · Cited by 265
Intensity correction in surface-coil MR imaging
Axel, L., Costantini, J., & Listerud, J. (1987) · American Journal of Roentgenology · doi:10.2214/ajr.148.2.418 · Cited by 244
Röschmann, P. (1987) · Medical Physics · doi:10.1118/1.595995 · Cited by 233
Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study
Özkan, M., Dawant, B. M., & Maciunas, R. J. (1993) · IEEE Transactions on Medical Imaging · doi:10.1109/42.241881 · Cited by 213
Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images
Kamber, M., Shinghal, R., Collins, D. L., Francis, G., & Evans, A. C. (1995) · IEEE Transactions on Medical Imaging · doi:10.1109/42.414608 · Cited by 211
Self-citationSled, J. G., & Pike, G. B. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.730409 · Cited by 190
Self-citationRetrospective correction of intensity inhomogeneities in MRI
Meyer, C. R., Bland, P. H., & Pipe, J. G. (1995) · IEEE Transactions on Medical Imaging · doi:10.1109/42.370400 · Cited by 178
Multiple surface identification and matching in magnetic resonance images
MacDonald, D. J., Avis, D., & Evans, A. C. (1994) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.185176 · Cited by 159
Self-citationCorrection of intensity nonuniformity in MR images of any orientation
Wicks, D., Barker, G. J., & Tofts, P. S. (1993) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(93)90023-7 · Cited by 155
The theory of the bird-cage resonator
Tropp, J. S. (1989) · Journal of Magnetic Resonance (1969) · doi:10.1016/0022-2364(89)90164-9 · Cited by 146
Local histogram correction of MRI spatially dependent image pixel intensity nonuniformity
DeCarli, C. S., Murphy, D., Teichberg, D., Campbell, G. A., & Sobering, G. (1996) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1880060316 · Cited by 138
Compensation for surface coil sensitivity variation in magnetic resonance imaging
Narayana, P. A., Brey, W. W., Kulkarni, M. V., & Sievenpiper, C. (1988) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(88)90401-8 · Cited by 131
Image non-uniformity in magnetic resonance imaging: its magnitude and methods for its correction
Condon, B. R., Patterson, J. C., Wyper, D., et al. (1987) · British Journal of Radiology · doi:10.1259/0007-1285-60-709-83 · Cited by 121
Automatic quantification of multiple sclerosis lesion volume using stereotaxic space
Zijdenbos, A. P., Evans, A. C., Riahi, F., Sled, J. G., Chui, J., & Kollokian, V. (1996) · Lecture notes in computer science · doi:10.1007/bfb0046984 · Cited by 113
Self-citationThe Least-squares Fitting of Cubic Spline Surfaces to General Data Sets
Hayes, J. G., & Halliday, J. R. (1974) · IMA Journal of Applied Mathematics · doi:10.1093/imamat/14.1.89 · Cited by 104
Phase and sensitivity of receiver coils in magnetic resonance imaging
McVeigh, E. R., Bronskill, M. J., & Henkelman, R. M. (1986) · Medical Physics · doi:10.1118/1.595967 · Cited by 99
Intensity correction of phased‐array surface coil images
Murakami, J. W., Hayes, C. E., & Weinberger, E. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350419 · Cited by 95
Surface coil MR imaging of the human brain with an analytic reception profile correction
Moyher, S. E., Vigneron, D. B., & Nelson, S. J. (1995) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1880050204 · Cited by 93
The Role of MRI in clinical trials of multiple sclerosis: Comparison of image processing techniques
Evans, A. C., Frank, J. A., Antel, J. P., & Miller, D. H. (1997) · Annals of Neurology · doi:10.1002/ana.410410123 · Cited by 91
Self-citationŠimunić, D., Wach, P., Renhart, W., et al. (1996) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.477704 · Cited by 82
An algorithm for compensation of surface-coil images for sensitivity of the surface coil
Haselgrove, J. C., & Prammer, M. G. (1986) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(86)90024-x · Cited by 78
Narayana, P. A., & Borthakur, A. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910330312 · Cited by 64
Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate
Brechbühler, C., Gerig, G., & Székely, G. (1996) · Lecture notes in computer science · doi:10.1007/bfb0046948 · Cited by 59
Statistical intensity correction and segmentation of MRI data
Wells, W. M., Grimson, W. E. L., Kikinis, R., et al. (1994) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.185172 · Cited by 55
Post‐acquisition correction of MR inhomogeneities
Lee, S., & Vannier, M. W. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910360215 · Cited by 42
Kollokian, V. (1996) · Spectrum Research Repository (Concordia University) · Cited by 41
An approach to three-dimensional image segmentation
Liou, S.-P., & Jain, R. (1991) · CVGIP Image Understanding · doi:10.1016/1049-9660(91)90014-g · Cited by 29
Sources of T1 variance in normal human white matter
Harvey, I., Tofts, P. S., Morris, J. K., et al. (1991) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(91)90096-5 · Cited by 21
Enhancement of T1 MR images using registration for signal averaging
Holmes, C. J., Hoge, R. D., Collins, D. L., et al. (1996) · NeuroImage · doi:10.1016/s1053-8119(96)80030-9 · Cited by 18
Guillemaud, R., & Brady, M. (1996) · Lecture notes in computer science · doi:10.1007/bfb0046943 · Cited by 13
On Generalized Cross Validation for Tensor Smoothing Splines
Schumaker, L. L., & Utreras, F. I. (1990) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0911042 · Cited by 13
MR Image Non-uneformity Correction Techniques
Glennon, D. T., Clarke, L. P., Velthuizen, R. P., et al. (2005) · doi:10.1109/iembs.1991.683854 · Cited by 6
Enhancement of T2 and proton density MR images using registration for signal averaging
Holmes, C. J., Hoge, R. D., Woods, R. P., et al. (1996) · NeuroImage · doi:10.1016/s1053-8119(96)80031-0 · Cited by 5
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
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
Dynamic mapping of human cortical development during childhood through early adulthood
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent, T. F., Herman, D. H., Clasen, L. S., Toga, A. W., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0402680101 · Cited by 5,604
The minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253
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
