17. The system of claim 16 , wherein GCC models are derived for at least one of three transformation classes comprising pure translation, scaling and translation, and similarity transformation.
16. A system for registering a pair of medical image data for achieving a high degree of spatial correspondence therebetween, the system comprising: a scanner configured to obtain at least first and second digitized images of an object; a memory configured to store data representative of first and second medical images; a computer configured to employ a 3D landmark detection method to determine a correspondence between a portion of the first digitized image and a portion of the second digitized image, the portion of the first digitized image comprises a point of interest selected by a user as a user-defined click point, the portion of the first digitized image being a location within the first digitized image where a registration accuracy must be higher than registration accuracy in other portions of the first digitized image, and the correspondence is determined using geometrical relations between the user-defined click point in the first digitized image and a predetermined point in the second digitized image; and the computer is further configured to use a saliency region detection method comprising: computing a set of scale-invariant saliency features for the first digitized image; computing a set of scale-invariant saliency features for the second digitized image; and modeling a geometric configuration context (GCC) comprising a Gaussian mixture having component mean and width as a function of a correspondences between the saliency features of the first and second digitized images, wherein the GCC represents the spatial likelihood of a point in the second digitized image corresponding to a user-defined click-point in the first image.