Registration of magnetic resonance brain images is a geometric operation that determines point-wise correspondences between two brains. It remains a difficult task due to the highly convoluted structure of the brain. This paper presents novel methods, Brain Image Registration Tools (BIRT), that can rapidly and accurately register brain images by utilizing the brain structure information estimated from image derivatives. Source and target image spaces are related by affine transformation and non-rigid deformation. The deformation field is modeled by a set of Wendland's radial basis functions hierarchically deployed near the salient brain structures. In general, nonlinear optimization is heavily engaged in the parameter estimation for affine/non-rigid transformation and good initial estimates are thus essential to registration performance. In this work, the affine registration is initialized by a rigid transformation, which can robustly estimate the orientation and position differences of brain images. The parameters of the affine/non-rigid transformation are then hierarchically estimated in a coarse-to-fine manner by maximizing an image similarity measure, the correlation ratio, between the involved images. T1-weighted brain magnetic resonance images were utilized for performance evaluation. Our experimental results using four 3-D image sets demonstrated that BIRT can efficiently align images with high accuracy compared to several other algorithms, and thus is adequate to the applications which apply registration process intensively. Moreover, a voxel-based morphometric study quantitatively indicated that accurate registration can improve both the sensitivity and specificity of the statistical inference results.