Contact surfaces of the talus were then identified based on bone spatial relationships. Image reconstruction, segmentation and bone labeling were conducted on each set of CT data to identify talus and its adjacent bones. Thirteen sets (9 males and 4 females) of CT section data were obtained. Priority, therefore, in the shape fitting with optimization is given to the contact surfaces of the talus. Modeling the bones as rigid is common, and modeling the contact surfaces only for the rigid body saves additional computational resources. In this research, we developed a computational talus model based on CT section image data, image reconstruction and segmentation, contact surface identification, standard shape fitting, and finite element auto meshing algorithms. Much of the previous research adopted a detailed-geometry approach in modeling bones that potentially contributed to the heavy computational costs. However, due to the substantial time in model building and the heavy computational costs from the complexity of the models, daily clinical application of these foot models has yet to be achieved. Computational foot models have significant application in surgical decision making, injury and disease diagnosis and prevention, sports performance analysis and footwear engineering.
