Piecewise Trilinear Deformation of Tomographic Models

In this work we introduce an iterative method that deforms brain models built from tomographic images. The deformation is used for normalization purposes: individual models are deformed to match the shape, orientation and internal morphology of a reference model. In this method the individual and the reference models are each enclosed in a cube which is subdivided to form a rectangular grid. The vertices in the individual model's grid are perturbed and the contents of each cell is then trilinearly mapped into a cube. The composite of all resulting cubes form the deformed model to be compared with the reference. The perturbations on the vertices are generated by a simulated annealing optimization technique. To maximize the performance, the models are represented in a multi-resolution fashion and the method is parallelized