Hyperspectral unmixing of Raman micro-images for assessment of morphological and chemical parameters in non-dried brain tumor specimens

Hyperspectral unmixing is a group of unsupervised algorithms to calculate spectral endmembers and the abundances of components in Raman images. 39 Raman images were collected from six glioma brain tumor specimens. The tumor grades ranged from astrocytoma WHO II to glioblastoma multiforme WHO IV. The abundance plots of the cell nuclei were processed by an image segmentation procedure to determine the average nuclei size, the number of nuclei, and the fraction of nuclei area. The latter two morphological parameters correlated with the malignancy. A supervised unmixing approach called linear least squares fitting with non-negativity constraints is introduced. Endmembers of the most abundant and most dissimilar component are defined. As a result the content of proteins, nucleic acids, and lipids, lipid to proteins ratios were determined in all Raman images. Except for the protein content, all chemical parameters correlated with the malignancy. We conclude that the morphological and chemical information offer new ways to develop Raman-based classification approaches that can complement diagnosis of brain tumors. The role of non-linear Raman modalities to speed-up image acquisition is discussed.