A Novel Automated Retinal Image Fusion using Adaptive Exploratory Algorithm and Mutual-Pixel-Count Maximization

A novel automated approach of the multi-modality retinal image registration and fusion has been developed. The new algorithm, which is reliable, robust, and time-efficient, has an automatic adaptation from frame to frame with few tunable threshold parameters. The registration is based on retinal vasculature extraction using canny edge detector, and control point identification at the vessel bifurcations using adaptive exploratory algorithm. Shape similarity criteria are employed to match the control points. MPC maximization based optimization has been developed to adjust the control points at the sub-pixel level. MPC, which is initially introduced by this study into the biomedical image fusion area, is the new measurement criteria for fusion accuracy. A global maxima equivalent result is achieved by calculating MPC local maxima with an efficient computation cost. The comparative study has shown the advantage of the new approach in terms of novelty, efficiency, and accuracy.