Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
The resolution of the Gibbs phenomenon for spherical harmonics
Mathematics of Computation
A Spatio-Temporal Modeling Method for Shape Representation
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
In vivo tracking of 3D organs using spherical harmonics and subspace clustering
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Learning sparse representation using iterative subspace identification
IEEE Transactions on Signal Processing
Real time tracking of 3D organ surfaces using single MR image and limited optical viewing
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Parametric representation of deformable object with complex surface has been a challenge in various medical applications for its demanding resource consumptions. This paper proposed an efficient algorithm to construct a compact basis for a sequence of deformed 3D organ, in which those surfaces can be sparsely represented with a small number of parameters. The key idea in this paper is to explore the correlations among the deformed surfaces of an organ and extract the principle basis for representation and reconstruction. Both theoretical analysis and extensive simulations verified that the presented algorithm yields a three-order magnitude reduction in computational and storage complexity relative to traditional approaches while maintaining high precision for surface reconstruction. The proposed algorithm can be used for organ deformation tracking and optimal sampling strategy design.