Multimodal Medical Images using Rigid Iconic Registration based on Flower Pollination Algorithm and Butterfly Optimization Algorithm

Abstract

One of the numerous challenges of modern image processing is image registration. Information from many images often emerges in slightly different forms and is highly compatible. Spatial alignment is crucial to merge essential and valuable information from several images properly. The term "registration" describes this procedure. Find a transformation that results in a model that closely resembles the reference image [1].Mainly, this work is concerned with implementing two optimization algorithms: the Flower Pollination Algorithm (FPA) and the Butterfly Optimization Algorithm (BOA). To measure the efficacy of these methods, we compare the transformed image to the original by computing the mutual information between the two. The effectiveness of these methods was assessed using SSIM, EQM, and MI measures. Results from the experiments indicate that the BOA outperforms the FPA.