## 4 Conclusions  
In this paper, a novel fusion method for AFAM image fusion was proposed based on grayscale inversion and best-fit intensity selection. Grayscale inversion aims to change the grayscale feature of the morphology image and avoid the tradeoff between morphology and acoustic information. The inversion process is improved by inverting a single image into a series of inverted images and \begin{table}
\begin{tabular}{c c c c c c} \hline  & **MI** & \(Q^{\text{AB/F}}\) & **FMI** & **VIFF** & **SD** \\ \hline
**LP** & \(1.38\times 10^{-15}\) & \(4.17\times 10^{-14}\) & \(8.07\times 10^{-12}\) & \(9.99\times 10^{-9}\) & \(1.83\times 10^{-4}\) \\ \hline
**N5 CT** & \(9.14\times 10^{-14}\) & \(8.71\times 10^{-10}\) & \(3.35\times 10^{-11}\) & \(8.38\times 10^{-8}\) & \(5.46\times 10^{-2}\) \\ \hline
**LP-SR** & \(7.01\times 10^{-17}\) & \(2.64\times 10^{-13}\) & \(5.51\times 10^{-12}\) & \(4.07\times 10^{-8}\) & \(5.42\times 10^{-4}\) \\ \hline
**FusionGAN** & \(1.02\times 10^{-11}\) & \(3.25\times 10^{-14}\) & \(7.40\times 10^{-10}\) & \(4.45\times 10^{-8}\) & \(1.64\times 10^{-1}\) \\ \hline \end{tabular}
\end{table} 
Table 6: Test between proposed and compared methods. \begin{table}
\begin{tabular}{c c c c c} \hline
**Image** & **LP** & **N5 CT** & **LP-SR** & **FusionGAN** & **Proposed** \\ \hline
1 & 36.9658 & 39.8320 & 39.9261 & 34.5990 & **40.1020** \\ \hline
2 & 38.4776 & 47.6098 & 47.4358 & 43.2366 & **50.6317** \\ \hline
3 & 37.7895 & 37.6134 & 41.4245 & 33.5233 & **42.3467** \\ \hline
4 & 41.5586 & 46.5872 & 47.7077 & 41.2347 & **50.8664** \\ \hline
5 & 31.9550 & 42.2352 & 37.7491 & 39.0611 & **48.8546** \\ \hline
6 & 33.1649 & 39.8875 & 41.0251 & 33.8348 & **45.9419** \\ \hline
7 & 28.9697 & 35.6636 & 33.4225 & 34.4395 & **41.0504** \\ \hline
8 & 33.3158 & 41.9951 & 42.7390 & 46.4726 & **47.0966** \\ \hline
9 & **37.9474** & 35.4992 & 31.9065 & 29.4996 & 34.1644 \\ \hline
10 & 9.4739 & 18.4304 & 8.5953 & **41.7376** & 12.6174 \\ \hline
11 & 40.2326 & **44.4862** & 42.3177 & 42.0241 & 38.2377 \\ \hline
12 & 33.8317 & 39.4966 & 36.3585 & 41.0672 & **44.4249** \\ \hline
13 & 38.0081 & 40.5067 & 38.7161 & 37.3651 & **42.8768** \\ \hline
14 & 43.4355 & **49.6466** & 43.2625 & 41.2975 & 44.2562 \\ \hline
15 & 36.2075 & 37.5342 & 39.7434 & 38.7902 & **45.0851** \\ \hline
16 & 35.6061 & 39.3488 & 42.3115 & 43.2114 & **48.3688** \\ \hline \end{tabular}
\end{table} 
Table 5: Standard deviation (SD) between proposed and compared methods. The bold represents the best value. selecting the most suitable one as the output, which could avoid the tradeoff between high-intensity morphology information and complex acoustic details. The image segmentation method based on [PERSON]'s approach segments the regions of cells and background, which can greatly help reduce the interruption of the morphology image and improve segmentation precision. The selection of the best-fit intensity is applied to measure the total information loss of the pre-fused image and choose the best one as the output of our method, and MRIL is used as the fusion parameter of optimization. Experiments on different fusion methods can prove that our method is the best at presenting the features of source images, and saving information from the AFAM images. The results of quantitative comparisons also demonstrated that the contrast of our fused images depends on the source of the acoustic images, which means that our method is designed to restore acoustic image in the region of the cell. The results indicate that our method is good at highlighting the cell and providing subcellular structure, which is beneficial for the analysis of the cell's structure. Currently, the quality of acoustic image limits the fusion performance, while in the future, we will focus on the fusion rules for the inverted and acoustic images and make our fusion model more robust and efficient. Conceptualization, X.L. and Z.C. ; methodology, Z.C. ; investigation, Z.C. and X.L. ; validation, Z.C. ; data curation, Z.C. ; resource, X.L. ; writing original draft preparation Z.C.