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HyperSpectral Skin Imaging

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Title: HyperSpectral Skin Imaging


1
HyperSpectral Skin Imaging Tianchen Shi, Prof.
Charles A. DiMarzio Department of Electrical and
Computer Engineering, Northeastern University,
Boston, MA 02115 This work was supported in
part by CenSSIS, the Center for Subsurface
Sensing and Imaging Systems, under the
Engineering Research Centers Program of the
National Science Foundation ( Award Number
EEC-9986821),
  • CenSISS Value Added
  • Current Status
  • Achieved fast imaging ability to obtain
    reasonable skin chromospheres concentrations, and
    oxygen saturation.
  • Obtained blood vessels general geometry
    information average diameter and depth of blood
    vessels.
  • Future Plans
  • Extend to a spatially resolved concentration
    mapping.
  • Explore blood vessels diameter and depth
    distributions
  • Obtain better light source and detection arrays
    for high accuracy measurements,and increase the
    accuracy and stability of the information
    extraction procedures.
  • Take more in-vivo data for different skin
    subjects.
  • Abstract
  • Chromosphere monitoring is an important aspect
    in most of skin studies, especially for
    hemoglobin in human skin, which is closely
    related to development and diagnosis of various
    skin diseases. However, recent studies reveals
    that hemoglobin concentration obtained by
    assuming homogeneous distribution may deviate
    from its true value. On the other hand, diameter
    and distribution of blood vessels in skin are
    also crucial in monitoring of blood pressure and
    body temperature. Among various optical skin
    imaging techniques, HyperSpectral Imaging is a
    fast, stable method to provide spatial and
    spectral information of skin tissue. In this
    poster, we present a Monte Carlo assisted
    HyperSpectral imaging method to meet both of the
    challenges mentioned above. Reflection spectrum
    measurements are used to determine absolute
    concentration of chromospheres with aid from a
    Monte Carlo model, and blood vessel information
    can be further derived by a correction model for
    the inhomogeneity in skin layers.
  • State of the Art
  • Izumi Nishidate, Muroran Institute of
    Technology, Japan 4. The group used a multiple
    regression analysis assisted by a homogeneous
    Monte Carlo model to extract chromospheres
    concentration for point based total reflection
    measurement.
  • W. Verkruysse, Department of Radiation Oncology,
    University hospital Rotterdam, Rotterdam, The
    Netherlands 3. The group was using a correction
    factor obtained by a single blood vessel
    assumption for a fiber based skin measurement.
  • Significance and Challenges
  • Linear multiple regression based HyperSpectral
    imaging may provide possibility of fast imaging
    over an area of skin.
  • Pre-computed inhomogeneous Monte Carlo model may
    allow to obtain general information of blood
    vessels at superficial skin layer.
  • The method may need very high accuracy and
    stability of the Monte Carlo model to solve the
    non-linear and inter dependent relationship
    between chromosphere concentration and obtained
    multiple regression coefficients.
  • Technical Approaches
  • Orthogonal polarization detection with
    incoherent HyperSpectral imaging for in-vivo
    human skin measurement.
  • Monte Carlo simulation for inhomogeneous layered
    skin model with very carefully selected
    parameters.
  • Fast 2-D imaging with linear multiple regression
    analysis and non-linear conversion by
    pre-computed Monte Carlo results.

Figure 2, Experiment Setup Layout
Figure 1, Two-layer skin model randomly
distributed blood vessels with diffusively
incident photon packets.
  • Skin Model
  • Modified Lambert-Beers Law
  • where A is absorption, S is scattering term, ?
    is molar concentration of prevailing
    chromospheres (melanin, hemoglobin), and L is
    mean free transport path length.
  • Monte Carlo assisted Multiple Regression
    Analysis
  • where am is linear multiple regression
    coefficients, a is a vector consisting of the
    combination of am for the first and the third
    order, and b is a conversion vector nonlinearly
    relating Cm and am , obtained with an
    inhomogeneous Monte Carlo skin model.
  • Inhomogeneity in skin model (blood vessels)

Figure 3, In-vivo human skin absorption data cube
Figure 4, Extracted oxygen saturation map,
average chromospheres volume concentration
melanin (4.30), total hemoglobin(0.32), average
blood vessels diameter (101um) and depth (164um)
Figure 5, Comparison of in-vivo spectrum and
Monte Carlo simulation with extracted parameters
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