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    SIGNAL PROCESSING FOR SHORT WAVE INFRARED (SWIR) RAMAN SPECTROSCOPY DIAGNOSIS OF CANCER

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    Genre
    Thesis/Dissertation
    Date
    2017
    Author
    Sun, Yu
    Advisor
    Patil, Chetan Appasaheb
    Committee member
    Spence, Andrew J.
    Pleshko, Nancy
    Department
    Bioengineering
    Subject
    Engineering, Biomedical
    Auto-fluorescence Background Subtraction
    Ingaas Detector
    Noise Reduction
    Outlier Detection
    Raman Spectroscopy
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2483
    
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    DOI
    http://dx.doi.org/10.34944/dspace/2465
    Abstract
    Raman spectroscopy is an effective optical analysis of the biochemically specific characterization of tissues without contrast agents or exogenous dyes. Applications of Raman spectroscopy include analysis and biomarker investigation, disease diagnosis and surgical guidance. One major challenge in Raman spectroscopy is removing inherent fluorescence background present in samples to acquire Raman signatures. In some tissues, like liver, kidney and darkly pigment skin, the auto-fluorescence background is strong enough to overwhelm the Raman peaks in conventional Near-Infrared (NIR) Raman systems. Recent publications have shown that using Raman systems with excitation sources with wavelengths beyond 830 nm and short-wave infrared (SWIR) InGaAs Array detectors resulted in dramatically reduced auto-fluorescence. The unique characteristics of Raman signals collected from SWIR systems versus NIR Raman systems requires inspection of the suitability of spectral pre-processing techniques. This thesis focused on the development of spectral processing techniques at three different steps; 1) detector background & noise reduction; 2) Auto-fluorescence background subtraction; 3) detection of outlier measurements to assist statistical classification. Detector background and noise reduction was compared between two different techniques, and a direct subtraction method resulted in better performance to reduce fixed pattern noise unique to InGaAs arrays. For the aim 2, three different algorithms for fluorescence background removal were developed, and a modified polynomial fitting method was found to be most appropriate for the low signal-to-noise (SNR) spectra. Finally, local outlier factor(LOF), a multivariate statistical outlier metric, was implemented in a two-stage fashion, and shown to be effective at identifying raw measurement errors and Raman spectra outliers. The overall outcome of this thesis was the evaluation of spectral processing techniques for SWIR Raman spectroscopy systems, and the development of specific techniques to optimize data quality and best prepare spectra for statistical analysis.
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