Coherent Raman hyperspectral imaging technologies have progressed dramatically in recent years, collecting 100’s to 10,000’s of spectra per second with the spectra breadth of traditional spontaneous Raman spectroscopy. There is, however, a lack in available analysis and processing capabilities to bridge the gap between spectroscopy and chemical imaging, in which the end-user is interacting with molecular targets of interest. In this talk we will discuss our latest developments towards this goal, in particular: spectral unmixing/endmember extraction methods and high-speed, high-throughput peak characterization (peak-finding and fitting). Spectral unmixing methods aim to uncover pure species spectra. Certain demonstrated methods, such as vertex component analysis (VCA) require at least 1 pure pixel per chemical is present in the image. Other methods rely on statistical or geometric methods to estimate the pure spectra when no pure pixels are present. In this presentation, we will quantitatively compare results using several state-of-the-art techniques (internally and externally-developed). To autonomously examine retrieved pure spectra, we have developed a high-speed peak finding and fitting algorithm capable of characterizing spectra in micro- to milliseconds, in order to interface with our developed database and data mining methods. Collectively, these developments enable high-speed, high throughput analysis of 1 or many images. Numerical and experimental demonstrations will be presented on an open-source numerical tissue phantom and ~900-color BCARS imagery of murine tissue and clinical specimens.
Charles H. Camp, Sean McIntyre, and Marcus T. Cicerone, "From spectroscopy to chemical imaging: machine learning for hyperspectral coherent Raman imagery (Conference Presentation)," Proc. SPIE 10498, Multiphoton Microscopy in the Biomedical Sciences XVIII, 104981E (Presented at SPIE BiOS: January 30, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2291080.5751624476001.
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