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My ICIP 2006 Schedule
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Paper Detail
| Paper: | WA-P3.6 |
| Session: | Biomedical Imaging |
| Time: | Wednesday, October 11, 09:40 - 12:20 |
| Presentation: |
Poster |
| Topic: |
Biomedical Imaging: Biomedical image segmentation and quantitative analysis |
| Title: |
SAMPLED-DATA H_INFINITY FILTERING FOR ROBUST KINEMATICS ESTIMATION: APPLICATIONS TO BIOMECHANICS-BASED CARDIAC IMAGE ANALYSIS |
| Authors: |
Shan Tong; Hong Kong University of Science and Technology | | | | | Albert Sinusas; Yale University School of Medicine | | | | | Pengcheng Shi; Hong Kong University of Science and Technology / Southern Medical University of China | | |
| Abstract: |
A sampled-data H_infinity filtering strategy is proposed for cardiac kinematics estimation from periodic medical image sequences. Stochastic multi-frame filtering frameworks are constructed to deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data in a coordinated fashion. As robustness is of paramount importance in cardiac motion estimation, this mini-max H_infinity strategy is particulary powerful for real-world problems where the types and levels of model uncertainties and data disturbances are not available a priori. For the hybrid cardiac analysis system with continuous dynamics and discrete measurements, the state estimates are predicted according to the continuous-time state equation between observation time points, and updated with the new measurements obtained at discrete time instants, yielding physically more meaningful and more accurate estimation results for the continuously evolving cardiac dynamics. The strategy is validated through synthetic data experiments to illustrate its advantages and on canine MR phase contrast images to show its clinical relevance. |
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