Title :
Topology adaptive deformable surfaces for medical image volume segmentation
Author :
McInemey, T. ; Terzopoulos, Demetri
Author_Institution :
Dept. of Math., Phys. & Comput. Sci., Ryerson Polytech. Inst., Toronto, Ont., Canada
Abstract :
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.
Keywords :
image segmentation; medical image processing; modelling; topology; T-surfaces; affine cell image decomposition; complex anatomic structures; efficient reparameterization mechanism; medical diagnostic imaging; medical image volume segmentation; model-based medical image analysis technique; topology adaptive deformable surfaces; Biomedical imaging; Deformable models; Geometry; Image analysis; Image decomposition; Image reconstruction; Image segmentation; Shape; Surface reconstruction; Topology; Algorithms; Brain; Humans; Magnetic Resonance Angiography; Models, Neurological; Surface Properties;
Journal_Title :
Medical Imaging, IEEE Transactions on