A distinguishing characteristic of normal and cancer cells is the difference in their nuclear chromatin content and distribution. This difference can be revealed by the transmission spectra of nuclei stained with a pH-sensitive stain. Here, we used hematoxylin–eosin (HE) to stain hepatic carcinoma tissues and obtained spectral–spatial data from their nuclei using hyperspectral microscopy. The transmission spectra of the nuclei were then used to train a support vector machine (SVM) model for cell classification. Especially, we found that the chromatin distribution in cancer cells is more uniform, because of which the correlation coefficients for the spectra at different points in their nuclei are higher. Consequently, we exploited this feature to improve the SVM model. The sensitivity and specificity for the identification of cancer cells could be increased to 99% and 98%, respectively. We also designed an image-processing method for the extraction of information from cell nuclei to automate the identification process.

中文翻译：

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基于细胞核光谱空间特征的高光谱图像分类检测肝癌细胞

正常细胞和癌细胞的一个显着特征是其核染色质含量和分布的差异。这种差异可以通过用 pH 敏感染料染色的细胞核的透射光谱来揭示。在这里，我们使用苏木精-伊红 (HE) 对肝癌组织进行染色，并使用高光谱显微镜从其细胞核中获得光谱空间数据。然后使用细胞核的透射光谱来训练用于细胞分类的支持向量机 (SVM) 模型。特别是，我们发现癌细胞中的染色质分布更均匀，因此细胞核中不同点的光谱相关系数更高。因此，我们利用此功能来改进 SVM 模型。鉴别癌细胞的敏感性和特异性可分别提高到99%和98%。我们还设计了一种图像处理方法，用于从细胞核中提取信息以自动化识别过程。