论文标题
通过监督和无监督的学习对布里渊成像数据的多元分析
Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning
论文作者
论文摘要
布里渊成像依赖于从高光谱数据集中可靠提取细微的光谱信息。迄今为止，主流练习一直在使用光谱特征的线路拟合来检索平均峰值移动和线宽参数。但是，良好的结果在很大程度上取决于足够的SNR，并且可能不适用于由光谱混合物组成的复杂样品。因此，在这项工作中，我们提出了各种多元算法的使用，这些算法可用于对高光谱数据进行监督或无监督分析，我们探索了幻影和活细胞中的高级图像分析应用程序，即无混合，分类和细分。结果图像显示出可提供更多的对比度和细节，并在时间表$ 10^2 $上获得的速度比安装快。估计的光谱参数与根据纯拟合计算的参数一致。
Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been using line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient SNR 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 $10^2$ faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting.
