Image registration and fusion are conducted using an automated approach, which applies the automatic adaptation from frame to frame with the threshold parameters. Rather than qualitative approach, quantitative measures have been proposed to evaluate outcomes of retina image fusion. Concepts of the discrete entropy, discrete energy, relative entropy, mutual information, uncertainty coefficient and information redundancy have been introduced. Both the Canny edge detector and control point identification are employed to extract retinal vasculature using the adaptive exploratory algorithms. The shape similarity criteria have been selected for control point matching. The Mutual-Pixel- Count maximization based optimal procedure has also been developed to adjust the control points at the sub-pixel level. Then the global maxima equivalent result has been derived by calculating Mutual-Pixel-Count local maxima. For two cases of image fusion practices, the testing results are evaluated on a basis of information theories where the satisfactory outcomes have been made.