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ENHANCED DATA REDUCTION, SEGMENTATION, AND SPATIAL MULTIPLEXING METHODS FOR HYPERSPECTRAL IMAGING

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2017, Doctor of Philosophy in Clinical-Bioanalytical Chemistry, Cleveland State University, College of Sciences and Health Professions.
A hyperspectral image is a dataset consisting of both spectra and spatial information. It can be thought of either as a full spectrum taken at many pixel locations on a sample or many images of the same sample, each at a different wavelength. In recent decades, hyperspectral imaging has become a routine analytical method due to rapid advances in instrumentation and technique. Advances such as the speed of data acquisition, improved signal-to-noise-ratio, improved spatial resolution, and miniaturization of the instrumentation have all occurred, making chemical imaging methods more robust and more widely used. The work presented here deals with three issues in the field of hyperspectral imaging: unassisted data processing that is chemically meaningful and allows for subsequent chemometric analyses, visualization of the data that utilizes the full colorspace of modern red, green, blue (RGB) displays, and data collection with improved signal-to-noise ratios and comparably short acquisition times. Hyperspectral image data processing is a fundamental challenge in the field. There is a need for reliable processing techniques that can operate on the large amount of data in a hyperspectral image dataset. Because of the large quantity of data, currently-used methods for data processing are problematic because of how time-consuming and calculation-intensive they are or because of increased error that is observed in the less-intensive methods. The work presented here includes a user-unassisted method for rapidly generating chemical-based image contrast from hyperspectral image data. Our method, reduction of spectral images (ROSI), is an effective hyperspectral image processing method. A full theoretical description of the method is given along with performance metrics. The description has been generalized to work with any number of wavelength dimensions and spectra. A concise protocol is put forth that will enable other researchers to utilize this method by following a short, simple list of steps. ROSI can also be used as a data reduction method, as it achieves a threshold information density in the spectral dimension for all image pixels. ROSI results are suitable for subsequent data analysis enabling ROSI to be performed alone or as a preprocessing data reduction step. This research also improves upon a spatially-multiplexed Raman imaging system based on the digital micromirror device (DMD). The system provides signal-to-noise ratio enhancement while maintaining laser powers below the damage threshold of the sample and comparably short acquisition times. In the work presented here, the spatial resolution of the DMD imager has been improved such that features with a width of 2.19µm could be resolved, whereas the previous limit was 7.81µm.
John Turner, II, Ph.D. (Advisor)
David Ball, Ph.D. (Committee Member)
Petru Fodor, Ph.D. (Committee Member)
Xue-Long Sun, Ph.D. (Committee Member)
Yan Xu, Ph.D. (Committee Member)
Aimin Zhou, Ph.D. (Committee Member)

Recommended Citations

Citations

  • Ergin, L. N. (2017). ENHANCED DATA REDUCTION, SEGMENTATION, AND SPATIAL MULTIPLEXING METHODS FOR HYPERSPECTRAL IMAGING [Doctoral dissertation, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu1501871494997272

    APA Style (7th edition)

  • Ergin, Leanna. ENHANCED DATA REDUCTION, SEGMENTATION, AND SPATIAL MULTIPLEXING METHODS FOR HYPERSPECTRAL IMAGING. 2017. Cleveland State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu1501871494997272.

    MLA Style (8th edition)

  • Ergin, Leanna. "ENHANCED DATA REDUCTION, SEGMENTATION, AND SPATIAL MULTIPLEXING METHODS FOR HYPERSPECTRAL IMAGING." Doctoral dissertation, Cleveland State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1501871494997272

    Chicago Manual of Style (17th edition)