  Image registration is a key operation in medical image processing, allowing a
plethora of applications. Mutual information (MI) is consolidated as a robust
similarity metric often used for medical image registration. Although MI
provides a robust medical image registration, it usually fails when the needed
image transform is too big due to MI local maxima traps. In this paper, we
propose and evaluate a generalized parametric MI as an affine registration cost
function. We assessed the generalized MI (GMI) functions for separable affine
transforms and exhaustively evaluated the GMI mathematical image seeking the
maximum registration range through a gradient descent simulation. We also
employed Monte Carlo simulation essays for testing translation registering of
randomized T1 versus T2 images. GMI functions showed to have smooth isosurfaces
driving the algorithm to the global maxima. Results show significantly
prolonged registration ranges, avoiding the traps of local maxima. We evaluated
a range of [-150mm,150mm] for translations, [-180{\deg},180{\deg}] for
rotations, [0.5,2] for scales, and [-1,1] for skew with a success rate of
99.99%, 97.58%, 99.99%, and 99.99% respectively for the transforms in the
simulated gradient descent. We also obtained 99.75% success in Monte Carlo
simulation from 2,000 randomized translations trials with 1,113 subjects T1 and
T2 MRI images. The findings point towards the reliability of GMI for long-range
registration with enhanced speed performance
