MRI data sets are corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The use of adiabatic pulses can remove the inhomogeneity effects on transmit, but coil and patient parameters still affect reception. We describe an automatic technique that not only improves the worst corruptions such as those introduced by surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets such as head scans without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects such as rf coil design, and patient dependent effects such as tissue attenuation and dielectric-induced resonances exper! enced in high field MRI. Patient dependent attenuation effects are also encountered in x-ray computed tomography. All of the above are examples of multiplicative inhomogeneities which result in low spatial frequency corruption of acquired volume data sets. While we concentrate on MR in the remainder of the paper, the algorithms and techniques described are directly applicable to CT as well. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation and resonance effects. Key Words: Inhomogeneity correction, intensity correction, background correction, uniformity correction, retrospective, image processing.