Medical image segmentation using topologically adaptable surfaces

Abstract

Efficient and powerful topologically adaptable deformable surfaces can be created by embedding and defining discrete deformable surface models in terms of an Affine Cell Decomposition (ACD) framework. The ACD framework, combined with a novel and original reparameterization algorithm, creates a simple but elegant mechanism for multiresolution deformable curve, surface, and solid models to “flow” or “grow” into objects with complex geometries and topologies, and adapt their shape to recover the object boundaries. ACD-based models maintain the traditional parametric physics-based formulation of deformable models, allowing them to incorporate a priori knowledge in the form of energy and force-based constraints, and provide intuitive interactive capabilities. This paper describes ACD-based deformable surfaces and demonstrates their potential for extracting and reconstructing some of the most complex biological structures from medical image volumes.