  Mutual information (MI) is the standard method used in image registration and
the most studied one but can diverge and produce wrong results when used in an
automated manner. In this study we compared the results of the ITK Mattes MI
function, used in 3D Slicer and ITK derived software solutions, and our own
MICUDA Shannon and Tsallis MI functions under the translation, rotation and
scale transforms in a 3D mathematical space. This comparison allows to
understand why registration fails in some circumstances and how to produce a
more robust automated algorithm to register medical images. Since our
algorithms were designed to use GPU computations we also have a huge gain in
speed while improving the quality of registration.
