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Research on Deconvolution of Subcellular Fluorescence Images

Author: KongYiYing
Tutor: YeDaTian
School: Tsinghua University
Course: Biomedical Engineering
Keywords: Subcellular Structure Fluorescence Image Deconvolution Imaging ImageQuality Evaluation
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Type: Master's thesis
Year: 2013
Downloads: 5
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Abstract


With the development of biological research, current study of cellular structurecannot meet the demand of cell research. Subcellular structures are detail structureswithin a cell. Subcellular structure always needs to be observed during the tissue andcell analysis. Therefore, observation of Subcellular structures is significant tounderstand tissues and cells. Nowadays, fluorescence imaging technology is a commonapproach used for subcellular structures imaging. The fluorescent image is alwaysindistinct. Therefore, it is very important to build a fast algorithm to restore thefluorescent image with no hardware cost and real-time imaging ability.The object of the thesis is to restore the subcellular fluorescence images by usingthe deconvolution imaging method, reduce the influence of the out-of-focusfluorescence information, and improve the definition of the fluorescence images. Alsothe research introduces the principles of several deconvolution imaging method.Through analysis and evaluation on the imaging results, we choose the most suitabledeconvolution imaging method for the subcellular fluorescent image restoration.In this thesis, we study the imaging model and the point spread function offluorescence microscopy imaging system. We describe the imaging physical model andthe statistical model of the microscopy, and analyze the influence of application ofdifferent models in the deconvolution imaging process. Also the research analyzes thecauses of the point spread function, and describes it with a mathematical model. Weobtain the point spread function by two means. According to the imaging system and theimaging environment, we estimate the point spread function with the theoreticalapproach. We also obtain the point spread function by experimental measurements,using the fluorescent microspheres as the point light source.In this thesis, we restore the subcellular fluorescence images by using severaldeconvolution imaging methods, including the commonly used inverse filtering, Wienerfiltering method, and also including Richardson-Lucy deconvolution method whichcomes from maximum likelihood theory and is widely used in fluorescent cell imagerestoration. We also evaluate the restored images. Finally, we evaluate and compare thequality of various deconvolution imaging methods from objective evaluation index such as mean square error and structural similarity.

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