Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Bayesian Networks
A novel parameter decomposition approach to faithful fitting of quadric surfaces
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
| Hi-index | 0.00 |
One of the most difficult steps of intramedullary nailing of femoral shaft fractures is distal locking - the insertion of distal transverse interlocking screws, for which it is necessary to know the position and orientation of the distal locking holes of the intramedullary nail. This paper presents a novel parameter decomposition approach for solving this problem using single calibrated X-ray image. The problem is formulated as a model-based optimal fitting process, where the to-be-optimized parameters are decomposed into two sets: (a) the angle between the nail axis and its projection on the imaging plane, and (b) the translation and rotation of the geometrical models of the distal locking holes around the nail axis. By using a hybrid optimization technique coupling an evolutionary strategy and a local search algorithm to find the optimal values of the latter set of parameters for any given value of the former one, we reduce the multiple-dimensional model-based optimal fitting problem to a one-dimensional search along a finite interval. We report the in-vitro experimental results, which demonstrate that the accuracy of our approach is adequate for successful distal locking of intramedullary nails.