Segmentation is fundamental for automated analysis of medical images. However, a unified approach for evaluation does not yet exist. Gold standards are often unapplicable because they require invasive preparations or tissue extraction. Empirical evaluations only reflect the conformity of segmentation with the subjective visual expectance of users, which is underlying inter- as well as intra-observer variabilities. This paper presents a consistent approach to create synthetic but realistic images with a-priori known object boundaries (silver standards), which are suitable for optimization nd evaluation of various segmentation algorithms. Rectangular example patches are collected for each tissue (interior, exterior, and a contour zone). Fourier amplitude and phase images are stored together with the mean gray value. For silver standard generation, a reference contour is either manually given or automatically extracted form real data applying the algorithm under evaluation. For each class of tissue, the amplitude of one patch is randomly combined with the perturbed phase of another. A randomly chosen mean from the same class is superimposed to the inverse Fourier transform. Numerous silver standards are obtained form only a few texture patches of each tissue. Based on microscopy, CT, and functional MRI data, the applicability of silver standards is proven in two, three, and four dimensions. They are analyzed with respect to systematic deviations. Minor deviations occur for two dimensional images while those for three or four dimensions are larger but still acceptable.