CT data sets surface extraction for biomechanical modeling of long bones
In modelling applications such as custom-made implants design is useful to have a surface representation of the anatomy of bones rather than the voxel-based representation generated by tomography systems. A voxel-to-surface conversion process is usually done by a 2D segmentation of the images stack. However, other methods allow a direct 3D segmentation of the CT or MRI data set. In the present work, two of these methods, namely the Standard Marching Cube (SMC) and the Discretized Marching Cube (DMC) algorithms, were compared in terms of local accuracy when used to reconstruct the geometry of a human femur. The SMC method was found to be more accurate than the DMC method. The SMC method was capable of reconstructing the inner and outer geometry of a human femur with a peak error lower than 0.9 mm and an average error comparable to the pixel size (0.3 mm). However, the large number of triangles generated by the algorithm may limit its adoption in many modelling applications. The peak error of the DMC algorithm was 1.6 mm but it produced ?70% less triangles than the SMC method. From the results of this study, it may be concluded that three dimensional segmentation algorithms are useful not only in visualisation applications but also in the creation of geometry models.
discretized marching cube modelfem contours ct-slices