title: Topology adaptive deformable surfaces for medical image volume segmentation.
abstract: Deformable models, which include deformable contours (the popular snakes) and deformable surfaces, are a powerful model-based medical image analysis technique. We develop a new class of deformable models by formulating deformable surfaces in terms of an affine cell image decomposition (ACID). Our approach significantly extends standard deformable surfaces, while retaining their interactivity and other desirable properties. In particular, the ACID induces an efficient reparameterization mechanism that enables parametric deformable surfaces to evolve into complex geometries, even modifying their topology as necessary. We demonstrate that our new ACID-based deformable surfaces, dubbed T-surfaces, can effectively segment complex anatomic structures from medical volume images.
authors.affiliation: Department of Mathematics, Physics, and Computer Science, Ryerson Polytechnic University, Toronto, Ont., Canada.
authors.forename: T
authors.initials: T
authors.lastname: McInerney
authors.forename: D
authors.initials: D
authors.lastname: Terzopoulos
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods* / Q000706:statistics & numerical data; D008959:Models, Neurological; D013499:Surface Properties