Atomic force acoustic microscopy (AFAM) can provide surface morphology and internal structures of the samples simultaneously, with broad potential in non-destructive imaging of cells. As the output of AFAM, morphology and acoustic images reflect different features of the cells, respectively. However, there are few studies about the fusion of these images. In this paper, a novel method is proposed to fuse these two types of images based on grayscale inversion and selection of best-fit intensity. First, grayscale inversion is used to transform the morphology image into a series of inverted images with different average intensities. Then, the max rule is applied to fuse those inverted images and acoustic images, and a group of pre-fused images is obtained. Finally, a selector is employed to extract and export the expected image with the best-fit intensity among those pre-fused images. The expected image can preserve both the acoustic details of the cells and the background’s gradient information well, which benefits the analysis of the cell’s subcellular structure. The experiments’ results demonstrated that our method could provide the clearest boundaries between the cells and background, and preserve most details from the morphology and acoustic images according to quantitative comparisons, including standard deviation, mutual information, Xydeas and Petrovic metric, feature mutual information, and visual information fidelity fusion.
Visual Information Fidelity with better Vision Models and better Mutual Information Estimates