Blackett Laboratory, Department of Physics, Imperial College London, London, UK.
Division of Physics and Applied Physics, Nanyang Technological University, Nanyang, Singapore.
J Biophotonics. 2021 Jul;14(7):e202000508. doi: 10.1002/jbio.202000508. Epub 2021 Apr 4.
Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient signal-to-noise ratio and may not be applicable in complex samples that consist of spectral mixtures. In this work, we thus propose the use of various multivariate algorithms that can be used to perform supervised or unsupervised analysis of the hyperspectral data, with which we explore advanced image analysis applications, namely unmixing, classification and segmentation in a phantom and live cells. The resulting images are shown to provide more contrast and detail, and obtained on a timescale ∼10 faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting.
布里渊成像依赖于从高光谱数据集中可靠地提取细微的光谱信息。迄今为止，主流的做法是使用光谱特征的线性拟合来获取平均峰值位移和线宽参数。然而，良好的结果在很大程度上取决于足够的信噪比，并且可能不适用于由光谱混合物组成的复杂样本。在这项工作中，我们因此提出使用各种多元算法，可以用于对高光谱数据进行有监督或无监督分析，我们探索了先进的图像分析应用，即在幻影和活细胞中进行解混、分类和分割。结果图像显示提供了更高的对比度和细节，并且获得的速度比拟合快约 10 倍。估计的光谱参数与从纯拟合计算得出的参数一致。
