With the advent of fast 3D magnetic resonance imaging (MRI) sequences, truly 3D volumes of data can be routinely acquired. While images produced by modalities such as positron emission tomography (PET;) and single photon emission computed tomography (SPECT) depict functional information, newer MRI techniques like magnetization-prepared rapid gradient-echo (MP-RAGE) capture a great deal of ana tomical detail [3,10]. Such 3D images are used by clinicians in two types of tasks, visualization and quLantifi cation. Visualization, in its most basic form, permits a user to see structures of interest within a volume of data. The "structures of interest" may not correspond to any physically visible phenomenon (e.g. the quan tity of blood flow to neural tissue in a functional image) but the process of visualization transforms the data into images which may then be displayed using computer graphics. On the other hand, quantification, while often depicted graphically, attempts to reduce the mass of data into numbers useful as clinical indicators. A major obstacle to both of these tasks is the prerequisite image segmentation. In order for the brain to be visualized beneath the overlying head, a segmentation step must determine which volume elements, or voxels, in the 3D head image correspond to the brain. Similarly, quantification requires the distinguishing of background voxels from voxels corresponding to the VOl. A growing number of clinical studies depend on volume measurements after segmentation. Examples include: tracking the progression/remission of disease processes (e.g. the size of intracranial tumors); evaluating the neuroanatomical abnormalities associated with schizophrenia (e.g. ventricular volumes); and determining atrophy associated with Alzheimer-type dementia and temporal lobe epilepsy (as in the hippocampal formation).