High Performance Adaptive Fidelity Algorithms for Multi-Modality Image Fusion

The novel high performance adaptive fidelity algorithms for automated multi-modality images’ feature detection, registration, and fusion is presented. The new fusion method, which consists of the Adaptive Fidelity Exploratory Algorithm (AFEA) and the Heuristic Optimization Algorithm (HOA), implements automatic adaptation from frame to frame with a few tunable thresholds. The new algorithms have been applied on two ophthalmologic modalities of nonhuman primate eyes – angiogram grayscale and fundus true color retinal optic nerve head (ONH) images. It has achieved an optimal fusion result by giving the visualization of fundus image with a complete angiogram overlay. Control points are detected at the vessel bifurcations using the AFEA algorithm. Shape similarity criteria are used to match the control points that represent same salient features of different images. The HOA algorithm adjusts the initial good-guess of control points at the sub-pixel level in order to maximize the objective function Mutual-Pixel-Count (MPC). This study has compared the presented method with the Centerline Control Point Selection Algorithm, Root-Mean-Square-Error Minimization (RMSE) objective function employed by the classic Iterative Closest Point (ICP) algorithm, Genetic Algorithm, and some other data fusion approaches and the results strengthen the AFEA and HOA algorithms in terms of novelty, automation, accuracy, and efficiency.