International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Structure from Motion Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Hybrid bronchoscope tracking using a magnetic tracking sensor and image registration
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Predictive camera tracking for bronchoscope simulation with CONDensation
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Deformable registration of bronchoscopic video sequences to CT volumes with guaranteed smooth output
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Observation-Driven adaptive differential evolution for robust bronchoscope 3-d motion tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
IPCAI'13 Proceedings of the 4th international conference on Information Processing in Computer-Assisted Interventions
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This paper presents a new hybrid bronchoscope tracking method that uses an electromagnetic position sensor, a sequential Monte Carlo sampler, and its evaluation on a dynamic motion phantom. Since airway deformation resulting from patient movement, respiratory motion, and coughing can significantly affect the rigid registration between electromagnetic tracking and computed tomography (CT) coordinate systems, a standard hybrid tracking approach that initializes intensitybased image registration with absolute pose data acquired by electromagnetic tracking fails when the initial camera pose is too far from the actual pose. We propose a new solution that combines electromagnetic tracking and a sequential Monte Carlo sampler to address this problem. In our solution, sequential Monte Carlo sampling is introduced to recursively approximate the posterior probability distributions of the bronchoscope camera motion parameters in accordance with the observation model based on electromagnetic tracking. We constructed a dynamic phantom that simulates airway deformation to evaluate our proposed solution. Experimental results demonstrate that the challenging problem of airway deformation can be robustly modeled and effectively addressed with our proposed approach compared to a previous hybrid method, even when the maximum simulated airway deformation reaches 23 mm.