Fully automatic determination of morphological parameters of proximal femur from calibrated fluoroscopic images through particle filtering

Authors:
Xiao Dong;Guoyan Zheng
Affiliations:
MEM Research Center, University of Bern, Bern, Switzerland;MEM Research Center, University of Bern, Bern, Switzerland
Venue:
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Year:
2006

Citing 5
Cited 1

Contour Tracking by Stochastic Propagation of Conditional Density

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Tracking Articulated Body by Dynamic Markov Network

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Visual Hand Tracking Using Nonparametric Belief Propagation

CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Stochastic simulation algorithms for dynamic probabilistic networks

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC

IEEE Transactions on Signal Processing

Automatic Extraction of Proximal Femur Contours from Calibrated X-Ray Images Using 3D Statistical Models

MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality

Quantified Score

Hi-index	0.00

Visualization

Abstract

A computational framework based on particle filter is proposed for fully automatic determination of morphological parameters of proximal femur from calibrated fluoroscopic images. In this framework, the proximal femur is decomposed into three components: (1) femoral head, (2) femoral neck, and (3) femoral shaft, among which structural constraints are defined according to the anatomical structure of the proximal femur. Each component is represented by a set of parameters describing its three-dimensional (3D) spatial position as well as its 3D geometrical shape. The constraints between different components are modeled by a rational network. Particle filter based inference is then used to estimate those parameters from the acquired fluoroscopic images. We report the quantitative and qualitative evaluation results on 10 dry cadaveric femurs, which indicate the validity of the present approach.