Multi-modal medical image fusion based on equilibrium optimizer algorithm and local energy functions

Multi-modal medical image fusion brings many benefits to clinical diagnosis and analysis because it creates favorable conditions for diagnostic imaging practitioners to make a more accurate diagnosis. According to our current knowledge, there are still some disadvantages to current image fusion approaches. The first one is that the fused images often have low contrast. The reason for this is several approaches use a weighted average rule for fusing low-frequency components. The second drawback is that the loss of detailed information in the fused image. This can be explained by the fact that the high-frequency components synthesized by the rules are not really effective. In this paper, two novel algorithms are proposed to tackle the above two disadvantages. The first algorithm is based on the Equilibrium optimizer algorithm (EOA) to find optimal parameters to fuse low-frequency components. This allows the fused image to have good contrast. The second algorithm is based on the sum of local energy functions using the Prewitt compass operator to create an efficient rule for the fusion of high-frequency components. This allows the fused image to significantly preserve details transferred from input images. Experimental results show that the proposed approach not only effective in significantly enhancing the quality of the fusion image but also preserving edge information carried from input images.

Automated summary

This paper introduces two novel algorithms to address the disadvantages of current image fusion approaches. The first algorithm utilizes the Equilibrium Optimizer Algorithm (EOA) to optimize parameters for fusing low-frequency components, resulting in improved contrast in the fused image. The second algorithm uses the sum of local energy functions with the Prewitt compass operator to efficiently fuse high-frequency components, leading to preservation of detailed information from input images.