A novel approach based on Three-scale image decomposition and Marine predators algorithm for multi-modal medical image fusion

Abstract

Multi-modal medical image fusion not only creates an image that preserves important information from the input images but also significantly improves in quality. This work contributes significantly to improving the ability of the physician to diagnose. So far, there have been many proposed approaches to improve efficiency for medical image fusion. However, some existing approaches still have certain drawbacks. The first drawback is that some vital information such as edges may be lost in the output image because of the high-frequency component fusion rules’ inefficiency. The second drawback is that the fused images often have low contrast because they have applied an average rule for the low-frequency components. In this study, a novel approach is introduced to overcome the aforementioned drawbacks, and it includes the following main steps. Firstly, the three-scale decomposition (TSD) technique is introduced to obtain the base and detail components. Secondly, a rule base on local energy function using the Kirsch compass operator is applied to fusing detail layers, which helps the output image preserve important information. Thirdly, the Marine predators algorithm (MPA) is utilized to fuse base layers by optimal parameters, allowing the output image to have good quality. To verify the proposed approach's effectiveness, we have utilized five state-of-the-art medical image fusion approaches and six image quality metrics for comparison. Experimental results show that the proposed approach significantly enhanced the fused image’s quality and preserved edge information.