Determination of Virtual Surgery Mass Point Spring Model Parameters Based on Genetic Algorithms
Chen, Y.; Hu, X.; Zhu, Q.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 32(6): 1202-1206
2015
ISSN/ISBN: 1001-5515 PMID: 27079087 Document Number: 684811
Mass point-spring model is one of the commonly used models in virtual surgery. However, its model parameters have no clear physical meaning, and it is hard to set the parameter conveniently. We, therefore, proposed a method based on genetic algorithm to determine the mass-spring model parameters. Computer-aided tomography (CAT) data were used to determine the mass value of the particle, and stiffness and damping coefficient were obtained by genetic algorithm. We used the difference between the reference deformation and virtual deformation as the fitness function to get the approximate optimal solution of the model parameters. Experimental results showed that this method could obtain an approximate optimal solution of spring parameters with lower cost, and could accurately reproduce the effect of the actual deformation model as well.