TY - GEN
T1 - Topology-preserving geometric deformable model on adaptive quadtree grid
AU - Bai, Ying
AU - Han, Xiao
AU - Prince, Jerry L.
PY - 2007
Y1 - 2007
N2 - Topology-preserving geometric deformable models (TGDMs) are used to segment objects that have a known topology. Their accuracy is inherently limited, however, by the resolution of the underlying computational grid. Although this can be overcome by using fine-resolution grids, both the computational cost and the size of the resulting contour increase dramatically. In order to maintain computational efficiency and to keep the contour size manageable, we have developed a new framework, termed QTGDMs, for topology-preserving geometric deformable models on balanced quadtree grids (BQGs). In order to do this, definitions and concepts from digital topology on regular grids were extended to BQGs so that characterization of simple points could be made. Other issues critical to the implementation of geometric deformable models are also addressed and a strategy for adapting a BQG during contour evolution is presented. We demonstrate the performance of the QTGDM method using both mathematical phantoms and real medical images.
AB - Topology-preserving geometric deformable models (TGDMs) are used to segment objects that have a known topology. Their accuracy is inherently limited, however, by the resolution of the underlying computational grid. Although this can be overcome by using fine-resolution grids, both the computational cost and the size of the resulting contour increase dramatically. In order to maintain computational efficiency and to keep the contour size manageable, we have developed a new framework, termed QTGDMs, for topology-preserving geometric deformable models on balanced quadtree grids (BQGs). In order to do this, definitions and concepts from digital topology on regular grids were extended to BQGs so that characterization of simple points could be made. Other issues critical to the implementation of geometric deformable models are also addressed and a strategy for adapting a BQG during contour evolution is presented. We demonstrate the performance of the QTGDM method using both mathematical phantoms and real medical images.
UR - http://www.scopus.com/inward/record.url?scp=35148831708&partnerID=8YFLogxK
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U2 - 10.1109/CVPR.2007.383335
DO - 10.1109/CVPR.2007.383335
M3 - Conference contribution
AN - SCOPUS:35148831708
SN - 1424411807
SN - 9781424411801
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
BT - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
T2 - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Y2 - 17 June 2007 through 22 June 2007
ER -