A handheld polarized hyperspectral imaging probe for tissue differentiation using spectral and polarization characteristics

Polarized hyperspectral imaging (PHSI) integrates spectral and polarization contrast to enable comprehensive tissue characterization. In this study, we validate a refined handheld PHSI probe that is more compact, lightweight, and capable of rapid image acquisition. The system was evaluated on freshly excised tissues from five male mice, focusing on four organ types-brain, tongue, thyroid, and testicle-chosen for their similar macroscopic appearance but distinct biochemical and microstructural properties. Spectral classification was performed using a one-dimensional convolutional neural network (1D-CNN) trained on total reflectance (Stokes S0) spectra. In parallel, polarization-resolved parameters-degree of linear polarization (DoLP) and degree of circular polarization (DoCP)-were derived to visualize structural and scattering differences among tissues. Despite the visual similarity among tissues, the model achieved high classification accuracy with an average overall accuracy of 95.3% across leave-one-out cross-validation folds. DoLP effectively differentiated glandular from adipose tissue, and highlighted muscle fiber orientation differences within tongue regions. DoCP distinguished blood-rich regions from surrounding glandular or fatty tissues, revealing higher DoCP in vascular areas and lower values in fat. These results confirm that spectral and polarization features offer complementary tissue contrast. This work demonstrates the potential of handheld PHSI for real-time tissue assessment and lays the foundation for future integration of spectral and polarization data in machine learning for clinical decision support.