A hybrid optimization approach for accelerated multimodal medical image fusion

Multimodal Medical Image Fusion is a key evolution in medical imaging. It contributes to improving diagnosis, providing better treatment, and reducing risk. Multimodal medical image fusion is a multi-objective due to the need of balancing factors like the weights of the fusion rules and the speed of the fusion process. While multi-objective particle swarm optimization has already been applied to solve this problem, it suffers from premature. It has been shown that the Darwinian Particle Swarm Optimization performs better than the classical Particle Swarm optimization by escaping the local optima. Therefore, this paper proposes a new approach based on the combination of variable-order fractional-order with multi-objective Darwinian Particle Swarm Optimization. Variable-order fractional-order improves the convergence rate of multi-objective Darwinian Particle Swarm Optimization by adjusting the particle velocity and position dynamically. Moreover, the new approach uses the gradient compass in the spatial domain to generate detailed images, further enhancing fusion quality. The proposed method is used to optimize both the fusion process weights and processing time. Experiments using the fusion of computed tomography along with magnetic resonance imaging show that the proposed technique outperforms existing techniques. Both the Inverted Generational Distance (IGD) and the Hyper-Volume (HV) metrics of the proposed multi-objective problem solution surpass the state-of-the-art showing the optimality of the provided solution. Additionally, the proposed solution image visual demonstrated high visual quality, efficient edge preservation, and the absence of noisy artefacts. Furthermore, our proposed fusion approach showed its suitability for real-time application, with a processing time not exceeding 0.085 seconds, outperforming other methods.