hyperspectR provides a complete R pipeline for biomedical hyperspectral imaging analysis -- from raw camera data to clinical tissue oxygenation maps.
# install.packages("remotes")
remotes::install_github("r-heller/hyperspectR")library(hyperspectR)
# Load example cube (synthetic 30x30 tissue scene, 61 bands, 430-910 nm)
cube <- hs_example_cube()
print(cube)
# Plot RGB composite
autoplot(cube, type = "rgb")
# Compute tissue oxygenation
sto2 <- hs_sto2(cube)
hs_plot_index(sto2, title = "StO2 (%)", palette = "sto2")
# Clinical 5-panel display (TIVITA-style)
hs_plot_clinical(cube)
# Launch interactive explorer
hs_run_app(cube)- I/O: Read ENVI, multi-channel TIFF, and Cubert .cu3s files
- Calibration: Dark correction, white reference normalization, bad pixel repair
- Preprocessing: Savitzky-Golay smoothing, SNV, MSC, spectral derivatives
- Biomedical indices: StO2, NPI, THI, TWI, custom normalized difference indices
- Analysis: PCA, MNF, SAM classification, SVM/RF pixel classification, Beer-Lambert unmixing
- Visualization: ggplot2-based spectral plots, clinical panel displays, interactive Shiny app
- Clinical focus: Intraoperative oxygenation mapping, compartment syndrome assessment
MIT