Algorithm for Automatic Nonlinear

Abstract

Introduction In order to average or compare signals from functional brain images across subjects, e.g., in the case of fMRI, PET, or MEG, it is necessary to register the images with respect to each other. Registration techniques have been used also from TMS target area determination [1] to automatic segmentation of MRI images [2]. One of the advantages of spatially normalized images is that the locations of activation can be reported as Euclidean coordinates within a standard space. With segmentation, the transformation can be used to move the segments from one image to another: this way the geometrical knowledge of the anatomical information is automatically applied in the segmentation. Ultimately, inter-subject registration creates a correspondence map consisting of three parameters for each voxel in the image. These parameters represent the x, y, and z coordinates of the corresponding point in another image, typically a template image defined in the Talairach space. The number of