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231223s2011 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2010.2092434
|2 doi
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|e rakwb
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|a eng
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|a Yeo, Si Yong
|e verfasserin
|4 aut
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|a Geometrically induced force interaction for three-dimensional deformable models
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|c 2011
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 19.08.2011
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|a Date Revised 21.04.2011
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
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|a Xie, Xianghua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sazonov, Igor
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nithiarasu, Perumal
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 20(2011), 5 vom: 15. Mai, Seite 1373-87
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
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|g volume:20
|g year:2011
|g number:5
|g day:15
|g month:05
|g pages:1373-87
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2010.2092434
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