激光与光电子学进展, 2024, 61 (6): 0618023, 网络出版: 2024-03-22  

快照式显微光谱成像系统及水藻分类识别研究

Research on Snapshot Microspectral Imaging System and Algae Classification Recognition
作者单位
1 长春理工大学物理学院,吉林 长春 130022
2 长春理工大学吉林省光谱探测科学与技术重点实验室,吉林 长春 130022
3 吉林求是光谱数据科技有限公司,吉林 长春 130000
摘要
目前的显微光谱成像系统的探测模块主要以推扫型光谱成像仪为主,无法进行动态微观样本的观测。基于超材料宽谱调制型光谱成像技术体制,使用该原理研制的快照式光谱相机作为探测模块,其与显微镜模块形成新型的快照式显微光谱成像系统。该系统可实时获取样本的光谱曲线与光谱图像信息。同时利用该系统获取不同藻类的吸收光谱曲线,进一步使用基于支持向量机的图像分割识别算法,对水中的动态藻类样本进行识别。共测试样本80个,预测结果准确率为100%,召回率为65.52%,为快照式光谱成像技术在显微领域的应用奠定基础。
Abstract
Current detection modules in microspectral imaging systems primarily comprise push-broom spectral imagers, which cannot observe dynamic microscopic samples. In this study, leveraging the capabilities of metamaterial broad-spectrum-modulated spectroscopic imaging technology, a snapshot spectroscopic camera was developed and used as a detector module to form a novel snapshot microscopic spectroscopic imaging system with a microscope module. This system enables real-time acquisition of spectral curves and image information from samples. Additionally, we used the developed system to obtain the absorption spectral curves of different algae and further used a image segmentation recognition algorithm based on a support vector machine to recognize dynamic algae samples in water. A total of 80 samples were tested in this experiment, yielding 100% accuracy and 65.52% recall for the prediction results. Thus this result forms the foundation for the application of snapshot spectral imaging technology in the field of microscopy.

李栋梁, 蔡红星, 任玉, 李霜, 花扬扬, 王婷婷, 周建伟, 曲冠男, 王朔, 曹洋铭, 张桁源. 快照式显微光谱成像系统及水藻分类识别研究[J]. 激光与光电子学进展, 2024, 61(6): 0618023. Dongliang Li, Hongxing Cai, Yu Ren, Shuang Li, Yangyang Hua, Tingting Wang, Jianwei Zhou, Guannan Qu, Shuo Wang, Yangming Cao, Hengyuan Zhang. Research on Snapshot Microspectral Imaging System and Algae Classification Recognition[J]. Laser & Optoelectronics Progress, 2024, 61(6): 0618023.

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