Fully Automatic Determination of Morphological Parameters of Proximal Femur from Calibrated Fluoroscopic Images Through Particle Filtering
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.
KeywordsFemoral Neck Femoral Head Bayesian Network Proximal Femur Particle Filter
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