A novel approach based on Grasshopper optimization algorithm for medical image fusion

The fusion of multi-modal medical images makes a significant contribution to clinical diagnosis and analysis because it allows diagnostic imaging practitioners to make a more accurate diagnosis. According to our current knowledge, there are some limitations of current medical image fusion approaches. The first limitation is that the use of a weighted average rule for fusing low-frequency components. This limitation leads to a decrease in the intensity of the brightness of the fused image. The second limitation is that the utilizing of fusion rules for high frequency components is not really optimal. This is likely to result in the loss of detailed information in the fused image. In this paper, a novel approach, including two algorithms, is proposed to address the above-mentioned limitations. The first algorithm is based on the Grasshopper optimization algorithm (GOA) to find optimal parameters with the aim of fusing low-frequency components. This allows the fused image to have good contrast. The second algorithm is based on the Kirsch 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 enhancing significantly the contrast of the fused image but also preserving edge information carried from input images to the composite image.

Automated summary

The proposed approach in this paper overcomes the limitations of current medical image fusion methods by using two algorithms to handle low-frequency and high-frequency components separately. This results in a fused image with improved contrast and preserved details, as demonstrated by experimental results.