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
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Automatic Extraction of Femur Contours from Calibrated Fluoroscopic Images
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Surface/Volume-Based Articulated 3D Spine Inference through Markov Random Fields
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Multimodal inference of articulated spine models from higher order energy functions of discrete MRFS
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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In this paper, we present an unsupervised 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 fluoroscopic 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 inference method. 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 experiments to compare the present unsupervised reconstruction scheme to a supervised one. An average mean error of 1.2 mm was found when the supervised reconstruction scheme was used. It increased to 1.3 mm when the unsupervised one was used. However, the unsupervised 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.