Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Significance: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.Aim: To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.Approach: We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.Conclusions: Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable

Multispectral upconversion luminescence intensity ratios for ascertaining the tissue imaging depth

Upconversion nanoparticles (UCNPs) have in recent years emerged as excellent contrast agents for in vivo luminescence imaging of deep tissues. But information abstracted from these images is in most cases restricted to 2-dimensions, without the depth information. In this work, a simple method has been developed to accurately ascertain the tissue imaging depth based on the relative luminescence intensity ratio of multispectral NaYF4:Yb3+,Er3+ UCNPs. A theoretical mode was set up, where the parameters in the quantitative relation between the relative intensities of the upconversion luminescence spectra and the depth of the UCNPs were determined using tissue mimicking liquid phantoms. The 540 nm and 650 nm luminescence intensity ratios (G/R ratio) of NaYF4:Yb3+,Er3+ UCNPs were monitored following excitation path (Ex mode) and emission path (Em mode) schemes, respectively. The model was validated by embedding NaYF4:Yb3+,Er3+ UCNPs in layered pork muscles, which demonstrated a very high accuracy of measurement in the thickness up to centimeter. This approach shall promote significantly the power of nanotechnology in medical optical imaging by expanding the imaging information from 2-dimensional to real 3-dimensional