<p>Hyperspectral imaging is widely used for obtaining optical
information of light absorbers (e.g. biochemical composition) in a variety of
specimens or tissues in a label-free manner. Acquiring and processing spectral
data using hyperspectral imaging usually requires advanced instrumentation such
as spectrometers, spectrographs or tunable color filters, which are not easily
adaptable in developing instrumentation for field-based applications. Also, use
of only RGB information from conventional cameras is not sufficient to obtain a
reliable correlation with the actual content of the analyte of interest. We
propose a new concept of ‘virtual hyperspectral imaging’ to reconstruct the
full reflectance spectra from RGB image data. This allows us to use only RGB
image data to determine detailed spatial distributions of analytes of interest.
More importantly, it simplifies instrumentation without requiring bulky and expensive
hardware. Using a data-driven approach, we apply multivariate regression to
reconstruct hyperspectral reflectance image data from RGB images obtained using
a conventional camera or a smartphone. </p>
<p> </p>
<p>In developing a reliable reconstruction matrix, it is critical
to obtain a training data set of the specimen of study under the same optical
geometry since the spectral reflectance and absorbance is sensitive to the
detection and illumination parameters. We designed an image-guided
hyperspectral system that can acquire both hyperspectral reflectance and RGB
data sets under the same imaging configuration to minimize any discrepancies in
the hyperspectral reflectance data acquired using different optical sensing
geometries. In our technology development, a telecentric lens that is commonly
used in machine vision systems but rarely in bioimaging, serves as a key
component for reducing unwanted scattering in biological tissue due to its
highly anisotropic scattering properties, by acting as a back-directional gating
component to suppress diffuse light. We evaluate our spectrometer-less
reflectance imaging method using RGB-based hyperspectral reconstruction
algorithm for integration into a smartphone application for non-invasive
hemoglobin analysis for anemia risk assessment in communities with limited
access to central laboratory tests.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7789535 |
| Date | 25 June 2020 |
| Creators | Michelle A. Visbal Onufrak (5930357) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Virtual_Hyperspectral_Imaging_Toward_Data-Driven_mHealth/7789535 |
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