Anatomical warping grew primarily out of the pattern recognition field where significant effort has been devoted to the representation of image ensembles. The general approach is to determine the transformation that is necessary to register or align two image datasets by deforming a typical image template into alignment with a target image of interest. The initial applications were to identify corresponding regions between the images. The classic example has been correlation of functional regions in the brain that are well-characterized in term of neuroanatomy with those in a target image that may represent a brain that has pathology in terms of development or disease. The approaches that are used are usually classified as either model-based or pixel-based. Model-based approaches typically require some segmentation of a surface in the 3D image dataset. This surface is then warped into alignment with features in the target image. The pixel-based approaches do not in general require a segmentation, but rather deform pixels or some sampling of the pixels. The latter approach is the one utilized in our technique.