1. An image processing system, for correlating shapes in multi-dimensional images (m-D), comprising image data processing means for estimating a similarity measure including computing means for: estimating two image signals (f( x ), g( y )) representing shapes defined in respective windows (W 1 , W 2 ) in two multi-dimensional images; using a Hermite Transform (HT) applied to both said image signals for performing an evaluation of two first sets of scalar valued Hermite coefficients (f I , g I , F I , G I ), from which a combination yields a transformed set of scalar valued Hermite coefficients {K I }; applying an inverse of the Hermite Transform (HT −I ) to the transformed set of scalar valued Hermite coefficients {K I } to compute a windowed correlation function (K( v )); and estimating a maximum of said windowed correlation function as the similarity measure to correlate the shapes; displaying the correlated shapes and processed images; and repeating the steps of determining the windowed correlation function, as many times as necessary in order to reach a best possible windowed correlation function by modifying at least one rotation matrices (R 1 , R 2 ), at least one scaling factor ({z k }) and by increasing a number of indices (I) if more accuracy is needed.