Topology adaptive deformable surfaces for medical image volume segmentation

• Published in: IEEE transactions on medical imaging Vol. 18; no. 10; pp. 840 - 850
• Main Authors: McInemey, T., Terzopoulos, D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.1999
• Subjects: Algorithms; Biomedical imaging; Brain - anatomy & histology; Deformable models; Geometry; Humans; Image analysis; Image decomposition; Image reconstruction; Image segmentation; Magnetic Resonance Angiography - methods; Magnetic Resonance Angiography - statistics & numerical data; Models, Neurological; Shape; Surface Properties; Surface reconstruction; Surfaces; Topology
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.811261

Deformable models, which include deformable contours (the popular snakes) and deformable surfaces, are a powerful model-based medical image analysis technique. The authors develop a new class of deformable models by formulating deformable surfaces in terms of an affine cell image decomposition (ACID). The authors' 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. The authors demonstrate that their new ACID-based deformable surfaces, dubbed T-surfaces, can effectively segment complex anatomic structures from medical volume images.