Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis
Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis
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DOI：
10.1016/j.bbadis.2003.12.006
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发表时间：
2004-03-02
影响因子：
6.2
通讯作者：
Diem, M
中科院分区：
文献类型：
--
作者：
Lasch, P;Haensch, W;Diem, M
本文采用三种不同的聚类算法对生物组织的红外光谱进行聚类。使用光谱从结直肠腺癌部分，我们展示了如何红外图像可以组装凝聚层次（AH）聚类（沃德的技术），模糊C-均值（FCM）聚类，和k-均值（KM）聚类。讨论了生物组织红外成像的实际问题，如光谱质量和数据预处理对图像质量的影响。此外，聚类算法的适用性的空间分辨显微光谱数据和不同的聚类图像和组织病理学之间的相关程度进行了比较。使用任何聚类算法显着增加了信息内容的红外图像，相比单变量方法的红外成像（官能团映射）。在聚类成像方法中，AH聚类（Ward算法）被证明是组织结构分化方面的最佳方法。(C)2004 Elsevier B. V.保留所有权利。
In this paper, three different clustering algorithms were applied to assemble infrared (IR) spectral maps from IR microspectra of tissues. Using spectra from a colorectal adenocarcinoma section, we show how IR images can be assembled by agglomerative hierarchical (AH) clustering (Ward's technique), fuzzy C-means (FCM) clustering, and k-means (KM) clustering. We discuss practical problems of IR imaging on tissues such as the influence of spectral quality and data pretreatment on image quality. Furthermore, the applicability of cluster algorithms to the spatially resolved microspectroscopic data and the degree of correlation between distinct cluster images and histopathology are compared.The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to univariate methods of IR imaging (functional group mapping). Among the cluster imaging methods, AH clustering (Ward's algorithm) proved to be the best method in terms of tissue structure differentiation. (C) 2004 Elsevier B.V. All rights reserved.