Weighting hyperspectral image data for improved multivariate curve resolution results
Weighting hyperspectral image data for improved multivariate curve resolution results
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DOI：
10.1002/cem.1170
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发表时间：
2008-09-01
影响因子：
2.4
通讯作者：
Pedroso, M. Cristina
中科院分区：
文献类型：
--
作者：
Jones, Howland D. T.;Haaland, David M.;Pedroso, M. Cristina
结合高光谱共聚焦荧光显微镜和多元曲线分辨率（MCR）提供了一个理想的系统，改善定量成像时，多个荧光团存在。然而，当多个荧光团之间存在高光谱和/或空间重叠时，多个噪声源的存在限制了MCR准确提取纯成分光谱的能力。以前，MCR结果通过对光谱图像进行泊松分布噪声加权来改进，但通常存在额外的噪声源。我们已经确定并量化了高光谱荧光图像中的所有主要噪声源。发现了两个主要噪声源：泊松分布噪声和探测器读取噪声。我们提出了量化探测器读取噪声方差和经验确定计算泊松噪声方差所需的电子倍增CCD （EMCCD）增益因子的方法。我们发现，适当地加权光谱图像数据来考虑两个噪声源，可以提高MCR的精度。在本文中，我们展示了三种加权方案应用于真实的高光谱玉米叶片图像和基于同一图像的模拟数据。将MCR应用于补偿两个主要噪声源的真实和模拟高光谱图像，大大提高了提取的纯发射光谱及其浓度，相对于未加权或仅泊松加权的MCR数据。因此，正确识别和计算高光谱图像中的主要噪声源可以改善MCR结果。这些方法是非常通用的，可以应用于光谱图像的多变量分析，无论何时使用CCD或EMCCD探测器。版权所有(C) 2008约翰威利父子有限公司
The combination of hyperspectral confocal fluorescence microscopy and multivariate curve resolution (MCR) provides an ideal system for improved quantitative imaging when multiple fluorophores are present. However, the presence of multiple noise sources limits the ability of MCR to accurately extract pure-component spectra when there is high spectral and/or spatial overlap between multiple fluorophores. Previously, MCR results were improved by weighting the spectral images for Poisson-distributed noise, but additional noise sources are often present. We have identified and quantified all the major noise sources in hyperspectral fluorescence images. Two primary noise sources were found: Poisson-distributed noise and detector-read noise. We present methods to quantify detector-read noise variance and to empirically determine the electron multiplying CCD (EMCCD) gain factor required to compute the Poisson noise variance. We have found that properly weighting spectral image data to account for both noise sources improved MCR accuracy. In this paper, we demonstrate three weighting schemes applied to a real hyperspectral corn leaf image and to simulated data based upon this same image. MCR applied to both real and simulated hyperspectral images weighted to compensate for the two major noise sources greatly improved the extracted pure emission spectra and their concentrations relative to MCR with either unweighted or Poisson-only weighted data. Thus, properly identifying and accounting for the major noise sources in hyperspectral images can serve to improve the MCR results. These methods are very general and can be applied to the multivariate analysis of spectral images whenever CCD or EMCCD detectors are used. Copyright (C) 2008 John Wiley & Sons, Ltd.