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Medical image registration is a pivotal application within the field of medical imaging. It entails the fusion of commonalities among data of disparate modalities into a unified coordinate system, thereby achieving complementary imaging information. This process plays a crucial role in scenarios such as accurate disease diagnosis, surgical guidance, patient monitoring, and radiological therapy. Due to the distinct characteristics exhibited by changes in the same region over time or across different modalities, swiftly and accurately identifying corresponding relationships between pre-registered images remains a formidable challenge. This paper proposes the incorporation of an enhanced Artificial Rabbits Optimization (ARO) algorithm as an agent within the framework of reinforcement learning. The method, referred to as RL-mARO, is employed for medical image registration. By utilizing Normalized Mutual Information (NMI) as a reward and penalty feedback for the similarity metric, the agent continuously adjusts and alters the learning strategies within the population, thereby progressively converging towards the correct registration direction. The contribution of reinforcement learning to ARO was validated on the IEEE CEC2020 benchmark dataset. Simultaneously, registration experiments were conducted on the multi-modal brain dataset, Retrospective Image Registration Evaluation Project (RIRE), as well as the single-modal fundus dataset, Fundus Image Registration Dataset (FIRE). The results of both registration experiments demonstrate that the RL-mARO model exhibits elevated robustness and registration accuracy. [ABSTRACT FROM AUTHOR]

Multimodal medical image registration involves multiple pieces of medical equipment collecting complementary information on the same content, resulting in information fusion. Beyond its theoretical significance, it has practical applications in areas such as disease diagnosis, patient follow-up, surgical navigation, and radiology. Determining the optimal geometric-space transformation parameters is a major challenge in this field. To address this, we propose the opposition-based Hunger Games Search (OHGS) algorithm, an improved version of the Hunger Games Search (HGS) algorithm. The OHGS algorithm aims to find the optimal translation, scaling, and shearing parameters that result in the highest similarity value between the images being registered. By incorporating opposition-based learning (OBL) and dynamically adjusting mutation criteria, the OHGS algorithm has improved exploration capabilities and the ability to escape local optima. In this study, OHGS was compared with 9 other algorithms developed in recent years on 23 well-known benchmark functions and 10 single-objective optimization functions from IEEE CEC2020. Furthermore, to achieve better registration accuracy and optimal alignment of the geometric space in multimodal medical images, OHGS and 8 other algorithms were tested using RIRE and BraTS datasets, optimizing the two similarity measures of Normalized Mutual Information (NMI) and Structural Similarity Index Measure (SSIM). The results showed that OHGS significantly improved performance on benchmark functions and multimodal medical image registration problems, thereby expanding the application range of the HGS algorithm. [ABSTRACT FROM AUTHOR]