Abstract—Phase-based methods are commonly applied in image registration. When working with phase-difference methods only a single scale is employed, although the algorithms are normally iterated over multiple scales, whereas phasecongruency methods utilize the the phase from multiple scales simultaneously. This paper presents an extension to phasedifference methods employing parallel scales to achieve more accurate displacements. Results are also presented clearly favouring the use of parallel scales over single scale in more than 95 % of the 120 tested cases. I.

Abstract A high performance adaptive fidelity approach for multi-modality Optic Nerve Head (ONH) image fusion is presented. The new image fusion method, which consists of the Adaptive Fidelity Exploratory Algorithm (AFEA) and the Heuristic Optimization Algorithm (HOA), is reliable and time efficient. It has achieved an optimal fusion result by giving the visualization of fundus image with a maximum angiogram overlay. Control points are detected at the vessel bifurcations using the AFEA. Shape similarity criteria are used to match the control points that represent same salient features of different images. HOA adjusts the initial good-guess of control points at the subpixel level in order to maximize the objective function Mutual-Pixel-Count (MPC). In addition, the performance of the AFEA and HOA algorithms was compared to the Centerline Control Point Detection Algorithm, Root Mean Square Error (RMSE) minimization objective function employed by the traditional Iterative Closest Point (ICP) algorithm, Genetic Algorithm, and some other existing image fusion approaches. The evaluation results strengthen the AFEA and HOA algorithms in terms of novelty, automation, accuracy, and efficiency.