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.

It has been proposed that the construction of a photosensitizer–polymer conjugate would lead to an increased selective retention of the drug in tumor tissue resulting in an enhancement of selective tumor destruction by light in photodynamic therapy. In this study the kinetics of a tetra‐pegylated derivative of meta‐tetra(hydroxyphenyl)chlorin (mTHPC–PEG) were compared with those of native meta‐tetra(hydroxyphenyl)chlorin (mTHPC) in a rat liver tumor model. In addition, the time course of bioactivity of both drugs was studied in normal liver tissue. Pegylation of mTHPC resulted in a two‐fold increase in the plasma half‐life time, a five‐fold decrease in liver uptake and an increase in the tumor selectivity at early time intervals after drug administration. However, although mTHPC concentrations in liver decrease rapidly with time, mTHPC–PEG liver concentrations increased as a function of time. This led to a loss of tumor selectivity at all but the earliest time points, whereas with mTHPC tumor selectivity increased with time. For both drugs the time course of bioactivity in the liver parallels drug concentration levels with extensive necrosis after irradiation of mTHPC–PEG‐sensitized liver tissue up to drug–light intervals of 120 h. It is concluded that on balance mTHPC–PEG does not appear to show any benefits over native mTHPC for the treatment of liver tumors, as normal liver tissue accumulates the compound. However, pegylation is a potentially promising strategy with an increase in tumor selectivity and reduced liver uptake if accumulation in the liver can be prevented.