## 3 Results and Analysis  
Firstly, we try to compare the fusion quality of different methods. As for Figure 6, three typical images are selected to intuitive results on the fusion performance. The first and second rows show the fusion method's ability to provide the details between the acoustic image. The third row requires the fusion method to make a distinction of two cells. The first two columns are morphology and acoustic images, the last column presents the fusion result of our method. The other columns are the results of the compared fusion methods. From the result, it is easy to conclude that our fused images are the best at preserving acoustic information. The inverted morphology part smartly highlights the shape of cell and avoids covering the acoustic details. LP and RP present similar fusion results. They can both acquire boundary and the acoustic information of cells, but they lack the acoustic information inside the cell. The fusion result of NSCT shows that the edge of the inner structures is sharp, and the shape of cells is easy to distinguish, but the contrast of the inner structure is low. FusionGAN is good at preserving morphology information, but it only keeps gradient information of acoustic images. As a result, the internal structures of the cells remain low contrast and hard to be found. Our method's fusion result is much different from the others, which is the result of grayscale inversion applied to morphology images. The area outside the cells is not the region of interest, so the strong intensivity of the inverted morphology image would cover the acoustic noise, which is why the region of the background is bright. The cells' region is thought to contain much acoustic information, and the low insensitivity of morphology information has high contract with the acoustic information and highlights the detail of the internal structures of cells. Figure 5: Segmentation result of the compared images. The red circles are the regions of cells. Moreover, the gradient from the bright background and the dark region of the cell forms the boundary. As a result, the obtained results by the proposed have sharp edges, more details, and enhanced contrast inside the internal structure and clear boundary of cells. The analysis of fusion images' features indicates that the application of grayscale inversion significantly highlights the cell and prevents the bright morphology pixel to cover the cell structure. To assess the fusion quality in the cells' region, we further give quantitative comparisons of the five methods. We first manually segment the morphology image to obtain the weight map of the cell. Then, we apply the weight map to pre-process the fused image, morphology image, and acoustic image. Next, the five fusion metrics are used to evaluate the fusion quality of the pre-processed fusion image. Tables 1-5 list the metrics of the 16 images; the 9 th image is _E. coli_ and the others are _S. aureus_. Table 6 shows the result of the \(t\)-test between our proposed method and the methods with the second-highest value. According to Tables 1-3, our method approximately has the most extensive MI, \(Q^{AB/F}\), and FMI. The largest MI implies that our method preserves the most information of the source images. Our method has the best MI because it is enhanced to save the intensities of the acoustic image in the region of the cell, which is much stronger than the morphology information. The largest \(Q^{AB/F}\) demonstrates that our fused image has rich edges information. Since the acoustic image provides most of the edge information, we can also consider that our fused image has the most considerable acoustic information. The largest FMI also demonstrates that our method has the most features including texture, edge strength, and contrast. Although the values of VIFF and SD do not have as large an advantage as the above three metrics, they still have the highest average among the compared methods. The results of the \(t\)-test imply the significance of our method.