Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.
Analyst. 2018 Feb 26;143(5):1147-1156. doi: 10.1039/c7an01888f.
Tissue histology utilizing chemical and immunohistochemical labels plays an important role in biomedicine and disease diagnosis. Recent research suggests that mid-infrared (IR) spectroscopic imaging may augment histology by providing quantitative molecular information. One of the major barriers to this approach is long acquisition time using Fourier-transform infrared (FTIR) spectroscopy. Recent advances in discrete frequency sources, particularly quantum cascade lasers (QCLs), may mitigate this problem by allowing selective sampling of the absorption spectrum. However, DFIR imaging only provides a significant advantage when the number of spectral samples is minimized, requiring a priori knowledge of important spectral features. In this paper, we demonstrate the use of a GPU-based genetic algorithm (GA) using linear discriminant analysis (LDA) for DFIR feature selection. Our proposed method relies on pre-acquired broadband FTIR images for feature selection. Based on user-selected criteria for classification accuracy, our algorithm provides a minimal set of features that can be used with DFIR in a time-frame more practical for clinical diagnosis.
组织学利用化学和免疫组织化学标记物在生物医药和疾病诊断中发挥着重要作用。最近的研究表明，中红外（IR）光谱成像技术可以通过提供定量分子信息来增强组织学。该方法的主要障碍之一是傅里叶变换红外（FTIR）光谱的采集时间较长。离散频率源的最新进展，特别是量子级联激光器（QCL），通过允许选择性地对吸收光谱进行采样，可以减轻这一问题。然而，当光谱样本数量最小化时，DFIR 成像才具有显著优势，这需要事先了解重要的光谱特征。在本文中，我们展示了使用基于 GPU 的遗传算法（GA）和线性判别分析（LDA）进行 DFIR 特征选择。我们提出的方法依赖于预先获取的宽带 FTIR 图像进行特征选择。根据用户选择的分类准确性标准，我们的算法提供了一组最小的特征，可用于 DFIR 在更适合临床诊断的时间框架内进行分类。
