Optimal Image Fusion Algorithm using Modified Grey Wolf Optimization amalgamed with Cuckoo Search, Levy Fly and Mantegna Algorithm

摘要

Image fusion is a well-known process in digital image processing. It is extensively used in medical imaging for clinical diagnosis. Clinical image diagnoses like MR-SPECT, MR-PET, MR-CT, and MR: T1-T2 are used day to day basis in medical imaging. Previously many researchers are tried to detect any specific object by using some heuristic optimization technique, but heuristic optimization technique has some fault in its searching process as well as the optimization process. So, it is not worked out. In recent days some researcher has tried meta-heuristic optimization technique in image fusion, but the problem of local optimization restricted there searching flow to find optimum search result. Medical imaging for clinical diagnosis required a high level of precision for detection of a known or unknown object in the human body, but lack of suitable optimization technique, as well as any dedicated hardware, makes the diagnosis process harder than ever. So, here, a version of the modified GW (grey wolf) algorithm with the help of the cuckoo search algorithm has proposed. That not only help the optimization process to find the object in the human body precisely but also allows doctors to take some action in real-time. The optimization algorithm is tasted by using MATLAB R2018b. The proposed design is synthesized using Xilinx Vivado 18.2 synthesis tool and simulated using ModelSim. The outcomes of the synthesis report and simulation of the circuit outshine other metaheuristic optimization approach. This MGWO is performed by using our own designed algorithm.