Although region and boundary techniques have been used for a wide variety of image understanding tasks, they rarely can be applied without a high-level agent to guide their operation. Consider for example, the generation of spurious boundaries in locations with steep gradients. These false boundaries can occur in locations where undersampling produces a stair-step or aliased image. Even if aliasing can be corrected, two- and three-dimensional segmenters are still susceptible to boundary tracking problems (boundary tracking is necessary in order to aggregate primitive elements, such as lines and surfaces, into complete anatomical structures or landmarks). Boundary tracking errors can occur at locations where edges intersect or where edges break off from one another. Another major limitation of these methods is that they fail to utilize or extract any shape information from the image objects (Section will describe a technique for understanding shape information). In general, some sort of high-level interaction is required to (1) provide a context for the operators, (2) bound the search space, (3) disambiguate conflicting results, and (4) account for missing information. Section will describe some approaches to deal with these limitations.