Dynamic real-time deformations using space & time adaptive sampling
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
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)
Sparse representation of deformable 3D organs
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Real time simulation of organ motions induced by breathing: first evaluation on patient data
ISBMS'06 Proceedings of the Third international conference on Biomedical Simulation
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
Sparse representation of deformable 3D organs with spherical harmonics and structured dictionary
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
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Deformable organ tracking has been a challenge in various medical applications. This paper proposes an algorithm for 3D organ tracking based on spherical harmonics (SH) and subspace clustering. The potential deformation subspaces are identified from training data, based on which an extremely low density sampling strategy and a low cost deformation construction method are designed. Both theoretical analysis and simulations verified that the presented tracking algorithm minimizes the number of sampling locations, storage and computation complexity, while maintaining high accuracy. The designed approach can be applied to in vivo 3D organ tracking and visualization during surgical intervention.