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The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI is caused by variations in the B1-field of the radio frequency (rf) coil. A three-step method was developed to model and then reduce the effect. First, using a least squares formulation, the inhomogeneity is modeled as a maximum variation order two polynomial. Second, in the log domain the polynomial model is subtracted from the actual patient data set resulting in a compensated data set. Lastly, the compensated data set is exponentiated and rescaled. Statistical comparisons indicate volumes of significant corruption undergo a large reduction in the inhomogeneity, whereas volumes of minimal corruption are not significantly changed. As applied to a patient data set the variances of the liver and the subcutaneous fat volumes are reduced 38% and 32%, respectively. The psoas muscle volume, contained in a relatively homogeneous portion of the B1-field, exhibited no significant change in variance. Acting as a preprocessor, the proposed technique can enhance the role of statistical segmentation algorithms in body MRI data sets.