Registration Algorithm:

Registration is completed automatically in a two step process. The first [27] accounts for the linear part of the transformation by using correlation between Gaussian-blurred features (described below) extracted from both volumes. After automatic linear registration, there remains a residual non-linear component of spatial mis-registration among brains that is due (mostly) to normal anatomical morphometric variability. In the second step, the   program estimates the 3D deformation field [2,3] required to account for this variability. The deformation field is built by sequentially stepping through the target volume in a 3D grid pattern. At each grid-node i, the deformation vector required to achieve local registration between the two volumes is found by optimization of 3 translational parameters (tx_i,ty_i,tz_i) that maximize the objective function evaluated only in the neighbourhood region surrounding the node. The algorithm is applied iteratively in a multi-scale hierarchy, so that image blurring and grid size are reduced after each iteration, thus refining the fit. (The algorithm is described in detail in [2,32]).