MODEL-BASED BOUNDARY ESTIMATION OF COMPLEX OBJECTS USING HIERARCHICAL ACTIVE SURFACE TEMPLATES

A method for the segmentation of complex, three-dimensional objects using hierarchical active surface templates is presented. The templates consist of one or more active surface models which are specified according to a priori knowledge about the expected shape and location of the desired object. This allows complex objects to be naturally modeled as collections of simple subparts which are geometrically constrained. The template is adaptively deformed by the three-dimensional image data in which it is initialized such that the template boundaries are brought into correspondence with the assumed image object. An external energy field is developed based on a vector distance transform such that the surfaces are deformed according to object shape. The method is demonstrated by the segmentation of the human brain from three-dimensional magnetic resonance images of the head given an a priori model of a normal brain.