We are involved in a comprehensive program to characterize atherosclerotic disease using multiple MR images having different contrast mechanisms (T1W, T2W, PDW, magnetization transfer, etc.) of human carotid and animal model arteries. We use specially designed intravascular and surface array coils that give high signal-to-noise but suffer from sensitivity inhomogeneity and significant noise. We present here a new non-parametric method for correcting the images without assumption of the number of different tissues. Intensity inhomogeneity is modeled with cubic spline and is locally optimized using an entropy criterion. Validation has been performed on a specially design neck phantom as well as actual MR scans on patient neck. This same algorithm has been successfully applied to the correction of very high resolution, intravascular coil images. The steep bias is corrected sufficiently to aid human interpretation of gray scales. It should also make possible computerized tissue classification.