Institute of Photonic Technology, Albert Einstein Straße 9, 07745, Jena, Germany.
Anal Bioanal Chem. 2013 Nov;405(27):8719-28. doi: 10.1007/s00216-013-7257-7. Epub 2013 Aug 11.
Hyperspectral unmixing is an unsupervised algorithm to calculate a bilinear model of spectral endmembers and abundances of components from Raman images. Thirty-nine 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 combination of spectral unmixing and non-negativity constrained linear least squares fitting is introduced to assess chemical parameters. First, endmembers of the most abundant and most dissimilar components were defined that represent all data sets. Second, the content of the obtained components' proteins, nucleic acids, lipids, and lipid to protein 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.
高光谱解混是一种无监督算法，可从拉曼图像中计算出光谱端元和组分丰度的双线性模型。从六个脑胶质瘤标本中采集了 39 张拉曼图像。肿瘤分级从星形细胞瘤 WHO II 到胶质母细胞瘤多形性 WHO IV。细胞核的丰度图通过图像分割程序进行处理，以确定平均细胞核大小、细胞核数量和细胞核面积分数。后两个形态参数与恶性程度相关。引入了光谱解混和非负约束线性最小二乘拟合的组合来评估化学参数。首先，定义了最丰富和最不同的成分的端元，这些端元代表了所有数据集。其次，在所有拉曼图像中确定了所获得成分的蛋白质、核酸、脂质和脂质与蛋白质比率的含量。除了蛋白质含量外，所有化学参数都与恶性程度相关。我们得出的结论是，形态和化学信息为开发基于拉曼的分类方法提供了新途径，可以补充脑肿瘤的诊断。讨论了非线性拉曼模式在加速图像采集方面的作用。
