The well-known variability in the distribution of high frequency electromagnetic fields in the human body causes problems in the analysis of structural information in high field magnetic resonance images. We describe a method of compensating for the purely intensity-based effects. In our simple and rapid correction algorithm, we first use statistical means to determine the background image noise level and the edges of the image features. We next populate all "noise" pixels with the mean signal intensity of the image features. These data are then smoothed by convolution with a gaussian filter using Fourier methods. Finally, the original data that are above the noise level are normalized to the smoothed images, thereby eliminating the lowest spatial frequencies in the final, corrected data. Processing of a 124 slice, 256 x 256 volume dataset requires under 70 sec on a laptop personal computer. Overall, the method is less prone to artifacts from edges or from sensitivity to absolute head position than are other correction techniques. Following intensity correction, the images demonstrated obvious qualitative improvement and, when subjected to automated segmentation tools, the accuracy of segmentation improved, in one example, from 35.3% to 84.7% correct, as compared to a manually-constructed gold standard.