Study On Subcellular Imaging And Image Fusion Based On Atomic Force Acoustic Microscopy

Organism is a complex organic whole,which is composed of a variety of cells with special structures and functions.Systematic,comprehensive and high-resolution characterization of cell morphology and subcellular structures are still challenging for the imaging technology for the study of the structure and function of life.In this paper,a systematic and in-depth study of non-invasive and efficient imaging method and image fusion algorithm based on atomic force acoustic microscopy(AFAM)is proposed.These methods can promote the non-invasive and highly efficient development,progress,and application of image post-processing technologies,and advance the better understanding of various physiological processes such as the cell biochemical reactions and the mechanical responses,which leads to the improvements of the related fields in life sciences and medicine.Cell imaging based on atomic force acoustic microscopy makes great demands on the accuracy and robustness of its internal acoustic imaging technology,which is the basis for subsequent research of the image fusion.Based on the better understanding of the AFAM,the theoretical analysis of acoustic transmission imaging was performed,and it was confirmed that the imaging resolution,contrast and imaging accuracy could be improved by increasing the acoustic frequency within a certain range.At the same time,we designed and prepared different samples with well-defined grooves/gold particles to confirm the accuracy and robustness of the internal imaging of transmitted sound waves.The experimental results show that the resolution of the internal imaging of acoustic waves can reach nanometer level,and the size and location of internal defects can be accurately detected.Secondly,according to the special requirements of high-resolution imaging of subcellular structures,we carried out a series of targeted optimizations on the entire AFAM imaging process,including sample preparation,imaging process,parameter selection,and result analysis.For a variety of cell samples,we designed specific sample preparation methods and optimized imaging parameters to shorten imaging time and improve image quality.The optimized parameters mainly include probe type,acoustic frequency,scanning speed,and feedback parameters.At the same time,in order to the better analyses of the experimental results,a color image fusion model based on RGB was designed.Through the study of sample preparation,parameter optimization and fusion model,a platform for imaging subcellular structures was established.Using this platform,a map of subcellular structures including eukaryotes and prokaryotes was established to describe the detailed structures of the samples with different sizes and provide the clear images of subcellular features,which can promote the development of human cell atlas research and expand the application of this microtechnology in biomedical research.Thirdly,the morphological image and the acoustic image of AFAM contain different specific information,and the image fusion algorithm can help accurate analysis and localization of subcellular structures.This chapter mainly uses non-subsampling contourlet transform(NSCT)to perform the multi-frequency decomposition of images.The sum-modified-Laplacian is chosen to fuse high-frequency components,and the rule of maximum absolute value is used to fuse low-frequency components.The experimental results show that this method is superior to other comparison methods in terms of three evaluation metrics including local structure similarity(L^AB/F),edge similarity measure(Q^AB/F)and general image quality index(Q^W),which illustrates the method can help preserve more cellular details,improve contrast,and achieve better consistency between pixels.Finally,the method of cell image fusion method based on convolutional neural network is studied,due to the superior representation capabilities of hierarchical features at different levels of abstraction.Therefore,this chapter studies the research of cell image fusion methods based on deep learning.In this study,we use the siamese network to solve the problem of artificial selection of fusion rules.We use the curvelet transform to decomposes the source images into different subbands,while the fusion rules obtained by the siamese network is used to fuse the low-frequency subbands and sum-modified-Laplacian is chosen to fuse high-frequency subbands.By performing experiments on six different types of the cells,our method can obtain images with clearer edges with better structural details preserved.This dissertation systematically studies the method of imaging the subcellular structures based on atomic force acoustic microscope.Through the study of acoustic internal imaging,subcellular structure characterization methods,image fusion based on multi-scale decomposition,and image learning methods based on deep learning,the great potential of this imaging method is proved,and the fusion algorithms can provide new ways for structural analysis.The AFAM-based subcellular imaging method has the advantages of wide imaging range,easy sample preparation,multi-color display,and non-destructive imaging,which can promote the development of human cell atlas project to a certain extent,and help explore the cell transfer mechanism and clinical manifestations.