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首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >Localization of embedded inclusions using detection of fluorescence: Feasibility study based on simulation data, LS-SVM modeling and EPO pre-processing
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Localization of embedded inclusions using detection of fluorescence: Feasibility study based on simulation data, LS-SVM modeling and EPO pre-processing

机译:利用荧光检测对内含物进行定位:基于模拟数据,LS-SVM建模和EPO预处理的可行性研究

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摘要

Fluorescence spectroscopy is a useful technique for tissue diagnostics and is also a promising tool in the characterization of embedded structures in tissue. The emitted fluorescence from an embedded inclusion, marked with a fluorescent compound, is affected by several factors as the light propagates through the medium to the tissue boundary, where the fluorescence light is detected. Tissue absorption, scattering and autofluorescence, as well as the size and depth of the inclusion, affect the detected fluorescence light. The aim of this study is to investigate if the size and location of a fluorescent inclusion could be determined using models based a combination of External Parameter Orthogonalisation (EPO) and Least Squares Support Vector Machine (LS-SVM). This can be very useful for data pre-processing before a full fluorescence tomography reconstruction. The data set consisted of simulated multispectral fluorescence, where depth and radius of a spherical fluorescent inclusion were varied as well as the fluorescence contrast and optical properties of the surrounding tissue. The results showed that the non-linear models based on LS-SVM can simultaneously predict both radius and depth. It was observed that EPO acts as a useful pre-processing tool on spectra for this nonlinear model and that it was necessary to perform EPO to be able to predict the depth with the LS-SVM model.
机译:荧光光谱法是用于组织诊断的有用技术,也是表征组织中嵌入结构的有前途的工具。当光通过介质传播到组织边界并检测到荧光时,嵌入的包含荧光化合物标记的夹杂物发出的荧光会受到多种因素的影响。组织吸收,散射和自发荧光以及包裹体的大小和深度会影响检测到的荧光。这项研究的目的是研究是否可以使用基于外部参数正交化(EPO)和最小二乘支持向量机(LS-SVM)组合的模型确定荧光夹杂物的大小和位置。这对于完全荧光层析成像重建之前的数据预处理非常有用。数据集包括模拟的多光谱荧光,其中球形荧光包含物的深度和半径以及周围组织的荧光对比度和光学特性均发生变化。结果表明,基于LS-SVM的非线性模型可以同时预测半径和深度。据观察,EPO可以用作此非线性模型光谱的有用预处理工具,并且必须执行EPO才能使用LS-SVM模型预测深度。

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