In this report the applicability of an improved method of image segmentation of infrared microspectroscopic data from histological specimens is demonstrated. Fourier transform infrared (FT‐IR) microspectroscopy was used to record hyperspectral data sets from human colorectal adenocarcinomas and to build up a database of spatially resolved tissue spectra. This database of colon microspectra comprised 4120 high‐quality FT‐IR point spectra from 28 patient samples and 12 different histological structures. The spectral information contained in the database was employed to teach and validate multilayer perceptron artificial neural network (MLP‐ANN) models. These classification models were then employed for database analysis and utilised to produce false colour images from complete tissue maps of FT‐IR microspectra. An important aspect of this study was also to demonstrate how the diagnostic sensitivity and specificity can be specifically optimised. An example is given which shows that changes of the number of teaching patterns per class can be used to modify these two interrelated test parameters. The definition of ANN topology turned out to be crucial to achieve a high degree of correspondence between the gold standard of histopathology and IR spectroscopy. Particularly, a hierarchical scheme of ANN classification proved to be superior for the reliable classification of tissue spectra. It was found that unsupervised methods of clustering, specifically agglomerative hierarchical clustering (AHC), were helpful in the initial phases of model generation. Optimal classification results could be achieved if the class definitions for the ANNs were carried out by considering the classification information provided by cluster analysis. Copyright © 2007 John Wiley & Sons, Ltd.

中文翻译：

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人工神经网络作为 FT-IR 显微成像的监督技术

在这份报告中，证明了一种改进的组织学标本红外显微数据图像分割方法的适用性。傅里叶变换红外 (FT-IR) 显微光谱用于记录人类结直肠腺癌的高光谱数据集，并建立空间分辨组织光谱数据库。该结肠显微光谱数据库包含来自 28 个患者样本和 12 个不同组织结构的 4120 个高质量 FT-IR 点光谱。数据库中包含的光谱信息用于教授和验证多层感知器人工神经网络 (MLP-ANN) 模型。然后将这些分类模型用于数据库分析，并利用 FT-IR 显微光谱的完整组织图生成假彩色图像。该研究的一个重要方面还在于展示如何具体优化诊断敏感性和特异性。给出了一个例子，表明每班教学模式的数量的变化可以用来修改这两个相互关联的测试参数。ANN 拓扑的定义对于实现组织病理学的黄金标准和 IR 光谱学之间的高度对应至关重要。特别是，人工神经网络分类的分层方案被证明对于组织光谱的可靠分类是优越的。发现无监督的聚类方法，特别是凝聚层次聚类 (AHC)，在模型生成的初始阶段很有帮助。如果通过考虑聚类分析提供的分类信息来进行人工神经网络的类定义，则可以获得最佳的分类结果。版权所有 © 2007 John Wiley & Sons, Ltd.