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.

Optical technologies are widely used for tissue sensing purposes. However, maneuvering conventional probe designs with flat-tipped fibers in narrow spaces can be challenging, for instance during pelvic colorectal cancer surgery. In this study, a compact side-firing fiber probe was developed for tissue discrimination during colorectal cancer surgery using diffuse reflectance spectroscopy. The optical behavior was compared to flat-tipped fibers using both Monte Carlo simulations and experimental phantom measurements. The tissue classification performance was examined using freshly excised colorectal cancer specimens. Using the developed probe and classification algorithm, an accuracy of 0.92 was achieved for discriminating tumor tissue from healthy tissue.

For a long time, steady-state reflectance spectroscopy measurements have been performed so that diffusion theory could be used to extract tissue optical properties from the reflectance. The development of subdiffuse techniques, such as Single Fiber Reflectance Spectroscopy and subdiffuse SFDI, provides new opportunities for clinical applications since they have the key advantage that they are much more sensitive to the details of the tissue scattering phase function in comparison to diffuse techniques. Since the scattering phase function is related to the subcellular structure of tissue, subdiffuse measurements have the potential to provide a powerful contrast between healthy and diseased tissue. In the subdiffuse regime, the interrogated tissue volumes are much smaller than in the diffuse regime. Whether a measurement falls within the diffuse or subdiffuse regime depends on tissue optical properties and the distance between the source and detector fiber for fiber-optic techniques or the projected spatial frequency for hyperspectral imaging and SFDI. Thus, the distance between source and detector fibers or the projected spatial frequency has important implications for clinical applications of reflectance spectroscopy and should be carefully selected, since it influences which tissue optical properties the technique is sensitive to and the size of the tissue volume that is interrogated. In this paper, we will review the opportunities and pitfalls in steady-state reflectance spectroscopy in the subdiffuse and the diffuse regime. The discussed opportunities can guide the choice of either the diffuse or subdiffuse regime for a clinical application, and the discussed pitfalls can ensure these are avoided to enable the development of robust diagnostic algorithms. We will first discuss the relevant basics of light-tissue interaction. Next, we will review all the tissue scattering phase functions that have been measured and investigate which scattering phase function models are representative of tissue. Subsequently, we will discuss the sensitivity of diffuse and subdiffuse techniques to tissue optical properties and we will explore the difference in the interrogation depth probed by diffuse and subdiffuse techniques.

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