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.

Real-time In Vivo Tissue Characterization with Diffuse Reflectance Spectroscopy during Transthoracic Lung Biopsy: A Clinical Feasibility Study.

PURPOSE: This study presents the first in vivo real-time tissue characterization during image-guided percutaneous lung biopsies using diffuse reflectance spectroscopy (DRS) sensing at the tip of a biopsy needle with integrated optical fibers. EXPERIMENTAL DESIGN: Tissues from 21 consented patients undergoing lung cancer surgery were measured intraoperatively using the fiber-optic platform capable of assessing various physical tissue properties highly correlated to tissue architecture and composition. In addition, the method was tested for clinical use by performing DRS tissue sensing during 11 routine biopsy procedures in patients with suspected lung cancer. RESULTS: We found that water content and scattering amplitude are the primary discriminators for the transition from healthy lung tissue to tumor tissue and that the reliability of these parameters is not affected by the amount of blood at the needle tip. In the 21 patients measured intraoperatively, the water-to-scattering ratio yielded a 56% to 81% contrast difference between tumor and surrounding tissue. Analysis of the 11 image-guided lung biopsy procedures showed that the tissue diagnosis derived from DRS was diagnostically discriminant in each clinical case. CONCLUSIONS: DRS tissue sensing integrated into a biopsy needle may be a powerful new tool for biopsy guidance that can be readily used in routine diagnostic lung biopsy procedures. This approach may not only help to increase the successful biopsy yield for histopathologic analysis, but may also allow specific sampling of vital tumor tissue for genetic profiling.