This paper presents a new technique for the automatic model-based segmentation of 3-D objects from volumetric image data. The development closely follows the seminal work of Cootes et al.  but presents various new solutions to come up with a true 3-D technique rather than a slice-by-slice 2-D processing. The segmentation system includes both the building of statistical models and the automatic segmentation of new image data sets via a restricted elastic deformation of models. Geometric models are derived from a sample set of image data which have been segmented by experts. The surfaces of these binary objects are converted into a parametric surface net which is normalized to get an invariant object-centered coordinate system. Surface descriptions are expanded into series of spherical harmonics which provide parametric representations of object shapes. Gray-level information is represented by 1-D profiles normal to the surface. The alignment is based on the well-accepted stereotactic coordinate system since the driving application is the segmentation of brain objects. Shape statistics are calculated from the parametric shape representations rather than from the spatial coordinates of sets of points. After initializing the mean shape in a new data set on the basis of the alignment coordinates, the model elastically deforms in accordance to displacement forces across the surface but is restricted only by shape deformation constraints. The technique has been applied to segment left and right hippocampal structures from a large series of 3-D magnetic resonance scans taken from a schizophrenia study.