Microscope imaging is a fundamental experimental technique in a number of diverse research fields, especially biomedical research. It begins with basic arithmetic operations that intend to reproduce the information contained in the experimental sample. With the rapid advancement in CCD cameras and microscopes (e.g. STORM, GSD), image processing algorithms that extract information more accurate and faster are highly desirable.
The overarching goal of this dissertation is to further improve image analysis algorithms. As most of microscope imaging applications start with fluorescence quantification, first we develop a quantification method for fluorescence of adsorbed proteins on microtubules. Based on the quantified result, the adsorption of streptavidin and neutravidin to biotinylated microtubules is found to exhibit negative cooperativity due to electrostatic interactions and steric hindrance. This behavior is modeled by a newly developed kinetic analogue of the Fowler-Guggenheim adsorption model. The complex adsorption kinetics of streptavidin to biotinylated structures suggests that the nanoscale architecture of binding sites can result in complex binding kinetics and hence needs to be considered when these intermolecular bonds are employed in self-assembly and nanobiotechnology.
In the second part, a powerful lock-in algorithm is introduced for image analysis. A classic signal processing algorithm, the lock-in amplifier, was extended to two dimensions (2D) to extract the signal in patterned images. The algorithm was evaluated using simulated image data and experimental microscopy images to extract the fluorescence signal of fluorescently labeled proteins adsorbed on surfaces patterned with chemical vapor deposition (CVD). The algorithm was capable of retrieving the signal with a signal-to-noise ratio (SNR) as low as -20 dB. The methodology holds promise not only for the measurement of adsorption events on patterned surfaces but in all situations where a signal has to be extracted from a noisy background in two or more dimensions.
The third part develops an automated software pipeline for image analysis, Fluorescencent Single Molecule Image Analysis (FSMIA). The software is customized especially for single molecule imaging. While processing the microscopy image stacks, it extracts physical parameters (e.g. location, fluorescence intensity) for each molecular object. Furthermore, it connects molecules in different frames into trajectories, facilitating common analysis tasks such as diffusion analysis and residence time analysis, etc.
Finally, in the last part, a new algorithm is developed for the localization of imaged objects based on the search of the best-correlated center. This approach yields tracking accuracies that are comparable to those of Gaussian fittings in typical signal-to-noise ratios, but with one order-of-magnitude faster execution. The algorithm is well suited for super-resolution localization microscopy methods since they rely on accurate and fast localization algorithms. The algorithm can be adapted to localize objects that do not exhibit radial symmetry or have to be localized in higher dimensional spaces.
Throughout this dissertation, the accuracy, precision and implementation of new image processing algorithms are highlighted. The findings not only further the theory behind digital image processing, but also further enrich the toolbox for microscopy image analysis.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8TB16MV |
| Date | January 2015 |
| Creators | He, Siheng |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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