Automated model-based bias field correction of MR images of the brain

The authors propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method the authors...

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Bibliographic Details
Published inIEEE transactions on medical imaging Vol. 18; no. 10; pp. 885 - 896
Main Authors Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.10.1999
Subjects
Algorithms
Automated
Automation
Bias
Biomedical imaging
Brain
Brain - pathology
Brain modeling
Humans
Image segmentation
Inhomogeneity
Iterative algorithms
Iterative methods
Magnetic resonance imaging
Magnetic Resonance Imaging - instrumentation
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - statistics & numerical data
Mathematical models
Models, Neurological
Neurophysiology
Physiological models
Polynomials
Probability distribution
Probability distributions
Random processes
Reproducibility of Results
Schizophrenia - diagnosis
Signal processing
Tissue
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Summary:The authors propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method the authors propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. The authors have validated the bias correction algorithm on simulated data and they illustrate its performance on various MR images with important field inhomogeneities. They also relate the proposed algorithm to other bias correction algorithms.
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ISSN:0278-0062
1558-254X
DOI:10.1109/42.811268