Near-infrared (NIR) spectroscopic measurement of blood and tissue chemistry often requires a large set of subject data
for training a prediction model. We have previously developed the principal component analysis loading correction
(PCALC) method to correct for subject related spectral variations. In this study we tested the concept of developing
PCALC factors from simulated spectra. Thirty, two-layer solid phantoms were made with 5 ink concentrations (0.004%-
0.02%), 2 μs' levels, and 3 fat thicknesses. Spectra were collected in reflectance mode and converted to absorbance by
referencing to a 99% reflectance standard. Spectra (5733) were simulated using Kienle's two-layer turbid media model
encompassing the range of parameters used in the phantoms. PCALC factors were generated from the simulated spectra
at one ink concentration. Simulated spectra were corrected with the PCALC factors and a PLS model was developed to
predict ink concentration from spectra. The best-matched simulated spectrum was identified for each measured phantom
spectrum. These best-matched simulated spectra were corrected with the PCALC factors derived from the simulated
spectra set, and they were used in the PLS model to predict ink concentrations. The ink concentrations were predicted
with an R2=0.897, and an estimated error (RMSEP) of 0.0037%. This study demonstrated the feasibility of using
simulated spectra to correct for inter-subject spectral differences and accurately determine analyte concentrations in
turbid media.
Noninvasive near infrared (NIR) spectroscopic measurement of muscle oxygenation requires the penetration of
light through overlying skin and fat layers. We have previously demonstrated a dual-light source design and
orthogonalization algorithm that corrects for inference from skin absorption and fat scattering. To achieve
accurate muscle oxygen saturation (SmO2) measurement, one must select the appropriate source-detector
distance (SD) to completely penetrate the fat layer. Methods: Six healthy subjects were supine for 15min to
normalize tissue oxygenation across the body. NIR spectra were collected from the calf, shoulder, lower and
upper thigh muscles with long SD distances of 30mm, 35mm, 40mm and 45mm. Spectral preprocessing with the
short SD (3mm) spectrum preceded SmO2 calculation with a Taylor series expansion method. Three-way
ANOVA was used to compare SmO2 values over varying fat thickness, subjects and SD distances. Results:
Overlying fat layers varied in thickness from 4.9mm to 19.6mm across all subjects. SmO2 measured at the four
locations were comparable for each subject (p=0.133), regardless of fat thickness and SD distance. SmO2
(mean±std dev) measured at calf, shoulder, low and high thigh were 62±3%, 59±8%, 61±2%, 61±4%
respectively for SD distance of 30mm. In these subjects no significant influence of SD was observed (p=0.948).
Conclusions: The results indicate that for our sensor design a 30mm SD is sufficient to penetrate through a
19mm fat layer and that orthogonalization with short SD effectively removed spectral interference from fat to
result in a reproducible determination of SmO2.
Content-based mass image retrieval technology, utilizing both shape and texture features, is investigated in this paper.
In order to retrieve similar mass patterns that help improve clinical diagnosis, the performance of mass retrieval using
curvature scale space descriptors (CSSDs) and R-transform descriptors was mainly studied. The mass contours in the
DDSM database (Univ. of South Florida) were preprocessed to eliminate curl cases, which is very important for the
extraction of features. The peak extraction method from a CSS contour map by circular shift and CSSDs matching
method were introduced. Preliminary experiments show that the performance of CSSDs and R-transform descriptors
outperform other features such as moment invariants, normalized Fourier descriptors (NFDs), and the combined texture
feature. By combining CSSDs with R-transform descriptors and the texture features based on Gray-level Co-occurrence
Matrices (GLCMs), the experiments show that the hybrid method gives a better performance in mass image retrieval
than CSSDs or R-transform descriptors.
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