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

IEEE Transactions on Medical Imaging · 1998 · Vol. 17(1) · pp. 87–97
John G. SledAlex ZijdenbosAlan C. Evans

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.

Advanced MRI Techniques and ApplicationsMRI in cancer diagnosisStatistical Methods and InferenceNormalization (sociology)Nonparametric statisticsMultiplicative functionIntensity (physics)Computer scienceAlgorithmArtificial intelligencePattern recognition (psychology)MathematicsStatistics

MeSH terms

BrainHumansMagnetic Resonance ImagingModels, Theoretical
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References
Pattern classification and scene analysis
IEEE Transactions on Automatic Control · 1974 · 3,412 citations
Morphometric analysis of white matter lesions in MR images: method and validation
IEEE Transactions on Medical Imaging · 1994 · 1,264 citations
Adaptive segmentation of MRI data
IEEE Transactions on Medical Imaging · 1996 · 1,275 citations
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