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首页> 外文期刊>Journal of Chemometrics >Trilinear analysis of images obtained with a hyperspectral imaging confocal microscope
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Trilinear analysis of images obtained with a hyperspectral imaging confocal microscope

机译：用高光谱成像共聚焦显微镜获得的图像的三线性分析

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Hyperspectral imaging confocal microscopy (HSI-CM) is a powerful tool for the analysis of cellular processes such as the immune response. HSI-CM is a data rich technique that routinely generates two-way data having a spectral domain and an image or concentration domain. Using a variety of modifications to the instrument or experimental protocols, one can readily produce three-way data with HSI-CM. These data are often amenable to trilinear analysis. For example we have used a time series of 18 images acquired during photobleaching of the fluorophores in an effort to identify fluorescence resonance energy transfer (FRET). The resulting images represent intensity as a function of concentration, wavelength and photodegradation in time, to which we apply our techniques of trilinear decomposition. We have successfully employed trilinear decomposition of photobleaching spectral image data from fixed A549 cells transfected with yellow and green fluorescent proteins (YFP and GFP) as molecular probes of cellular proteins involved in the cellular immune response. While useful in the interpretation biological processes, the size of the data generated with the HSI-CM can be difficult to manage computationally. The 208 X 204 X 512 X 18 elements in the image data require careful processing and efficient analysis algorithms. Accordingly, we have implemented fast algorithms that can quickly perform the trilinear decomposition. In this paper we describe how three-way data are produced and the methods we have used to process them. Specifically, we show that co-adding spectra in a spatial neighborhood is a highly effective method for improving the performance of these algorithms without sacrificing resolution.
机译：高光谱成像共聚焦显微镜（HSI-CM）是用于分析细胞过程（例如免疫应答）的强大工具。 HSI-CM是一种数据丰富的技术，通常会生成具有光谱域和图像或浓度域的双向数据。通过对仪器或实验方案进行多种修改，可以轻松地使用HSI-CM生成三向数据。这些数据通常适用于三线性分析。例如，我们使用了在荧光团光漂白过程中获得的18张图像的时间序列，以试图确定荧光共振能量转移（FRET）。所得图像将强度表示为时间，浓度和波长以及光降解的函数，我们将三线性分解技术应用于此。我们已经成功地使用了来自固定染有黄色和绿色荧光蛋白（YFP和GFP）的A549细胞的光漂白光谱图像数据的三线性分解，作为参与细胞免疫反应的细胞蛋白的分子探针。尽管在解释生物学过程中很有用，但使用HSI-CM生成的数据大小可能难以通过计算进行管理。图像数据中的208 X 204 X 512 X 18元素需要仔细的处理和有效的分析算法。因此，我们实现了可以快速执行三线性分解的快速算法。在本文中，我们描述了如何生成三向数据以及用于处理它们的方法。具体而言，我们表明在空间邻域中共加频谱是一种在不牺牲分辨率的情况下提高这些算法性能的高效方法。

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