Nguyen Thi Nguyet Que, Jeannesson Pierre, Groh Audrey, Guenot Dominique, Gobinet Cyril
Université de Reims Champagne-Ardenne, Equipe MéDIAN-Biophotonique et Technologies pour la Santé, UFR de Pharmacie, 51 rue Cognacq-Jay, 51096 Reims, France.
Analyst. 2015 Apr 7;140(7):2439-48. doi: 10.1039/c4an01937g.
Fourier-transform infrared (FTIR) spectral imaging is currently used as a non-destructive and label-free method for analyzing biological specimens. However, to highlight the different tissue regions, unsupervised clustering methods are commonly used leading to a subjective choice of the number of clusters. Here, we develop a hierarchical double application of 9 selected crisp cluster validity indices (CCVIs) using K-Means clustering. This approach when tested first on an artificial dataset showed that the indices Pakhira-Bandyopadhyay-Maulik (PBM) and Sym-Index (SI) perfectly estimated the expected 9 sub-clusters. Then, the concept was applied to a real dataset consisting of FTIR spectral images of normal human colon tissue samples originating from 5 patients. PBM and SI were revealed to be the most efficient indices that correctly identified the different colon histological components including crypts, lamina propria, muscularis mucosae, submucosa, and lymphoid aggregates. In conclusion, these results strongly suggest that the hierarchical double CCVI application is a promising method for automated and informative spectral histology.
傅里叶变换红外（FTIR）光谱成像目前用作分析生物样本的无损且无标记方法。然而，为了突出不同的组织区域，通常使用无监督聚类方法，这导致聚类数量的选择具有主观性。在此，我们使用K均值聚类开发了9个选定的清晰聚类有效性指标（CCVI）的分层双重应用。该方法首先在人工数据集上进行测试，结果表明帕希拉 - 班迪奥帕德海 - 莫利克（PBM）指标和对称指数（SI）能够完美估计预期的9个子聚类。然后，将该概念应用于由来自5名患者的正常人结肠组织样本的FTIR光谱图像组成的真实数据集。结果表明，PBM和SI是最有效的指标，能够正确识别不同的结肠组织学成分，包括隐窝、固有层、黏膜肌层、黏膜下层和淋巴聚集物。总之，这些结果强烈表明，分层双重CCVI应用是一种用于自动化和信息丰富的光谱组织学的有前途的方法。
