Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Active shape models—their training and application
Computer Vision and Image Understanding
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Nonrigid 3-D/2-D Registration of Images Using Statistical Models
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
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In this paper, we present a particle filter based 2D/3D reconstruction scheme combining a parameterized multiple-component geometrical model and a point distribution model, and show its application to automatically reconstruct a surface model of a proximal femur from a limited number of calibrated X-ray images with no user intervention at all. The parameterized multiple-component geometrical model is regarded as a simplified description capturing the geometrical features of a proximal femur. Its parameters are optimally and automatically estimated from the input images using a particle filter based algorithm. The estimated geometrical parameters are then used to initialize a point distribution model based 2D/3D reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. We designed and conducted in vitro and in vivo to compare the present automatic reconstruction scheme to a manually initialized one. An average mean reconstruction error of 1.2 mm was found when the manually initialized reconstruction scheme was used. It increased to 1.3 mm when the automatic one was used. However, the automatic reconstruction scheme has the advantage of elimination of user intervention, which holds the potential to facilitate the application of the 2D/3D reconstruction in surgical navigation.