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Published Articles >> Table of Contents >> Abstract
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 1
pp. 394-399
Globally Optimal Segmentation of Interacting Surfaces with Geometric Constraints
Kang Li, University of Iowa
Xiaodong Wu, University of Texas — Pan American
Danny Z. Chen, University of Notre Dame
Milan Sonka, University of Iowa
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.117
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| Abstract |
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Efficient detection of globally optimal surfaces representing object boundaries in volumetric datasets is important and remains challenging in many medical image analysis applications. We have developed an optimal surface detection method that is capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and several geometric constraints defining the surface smoothness and interrelations. The method solves the surface detection problems by transforming them into computing minimum s-t cuts in the derived edge-weighted directed graphs. The proposed algorithm has low-order polynomial complexity and is computationally efficient. The method has been validated on over 100 computer generated volumetric images and 96 CT-scanned datasets of different-sized plexiglas tubes, yielding highly accurate results (mean signed error of the measured inner- and outer-diameters of the plexiglas tubes was 0.21 ± 3.20%). Our approach can be readily extended to higher dimensional image segmentation.
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 Additional Information
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Citation:
Kang Li, Xiaodong Wu, Danny Z. Chen, Milan Sonka,
"Globally Optimal Segmentation of Interacting Surfaces with Geometric Constraints,"
cvpr,
pp. 394-399,
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 1,
2004
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