  Application of independent component analysis (ICA) as an unmixing and image
clustering technique for high spatial resolution Raman maps is reported. A
hyperspectral map of a fixed human cell was collected by a Raman micro
spectrometer in a raster pattern on a 0.5um grid. Unlike previously used
unsupervised machine learning techniques such as principal component analysis,
ICA is based on non-Gaussianity and statistical independence of data which is
the case for mixture Raman spectra. Hence, ICA is a great candidate for
assembling pseudo-colour maps from the spectral hypercube of Raman spectra. Our
experimental results revealed that ICA is capable of reconstructing false
colour maps of Raman hyperspectral data of human cells, showing the nuclear
region constituents as well as subcellular organelle in the cytoplasm and
distribution of mitochondria in the perinuclear region. Minimum preprocessing
requirements and label-free nature of the ICA method make it a great unmixed
method for extraction of endmembers in Raman hyperspectral maps of living
cells.

Quantitative Methods;Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing