Active shape models—their training and application
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
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
VIS-a-VE: visual augmentation for virtual environments in surgical training
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
Region flow: a multi-stage method for colonoscopy tracking
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
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This paper investigates the use of Active Shape Models (ASM) to capture the variability of the intra-thoracic airway tree. The method significantly reduces the dimensionality of the non-rigid 2D/3D registration problem and leads to a rapid and robust registration framework. In this study, EM tracking data has been also incorporated through a probabilistic framework for providing a statistically optimal pose given both the EM and the image-based registration measurements. Comprehensive phantom experiments have been conducted to assess the key numerical factors involved in using catheter tip EM tracking for deformable 2D/3D registration.