An improvised coral reef optimization-based image registration using modified mutual information method

Abstract

Computer visualisation and medical applications require image registration. It includes transforming collective picture data into the familiar coordinate scheme. Metaheuristic-based methods were developed to explain the issue and improve the efficiency and accuracy of conventional image registration techniques due to their limitations. We describe a hybrid medical image registration technique using bio-inspired meta-heuristic algorithms: Betteroffspring and multi-crossover strategies increase convergent time and solution quality with an improvised coral reef optimization with modified mutual information (ICOR-MMI) algorithm. This new optimization method (ICORMMI) proposes that coral reefs expand, compete for space, and reproduce. A linear weighted sum of image intensity and contour flow model intensity is added to the mutual information calculation. Including statistical and spatial image data improves image registration. The established technique has been tested and verified using multiple medical image data sets, some of which contain single-modality and mixed-modality images (CT, MRI). The registration validates the proposed model's accuracy and efficiency and shows posture's contribution by incorporating statistical and geographical image data. This strategy is adapted to the real-coding problem and tested for realtime issues. The ICOR-MMI algorithm-based hybrid approach outperforms current results in time efficiency and toughness.