|
|
|
|
|
|
| Research and Development of Microscopic Hyperspectral Imaging in
Biological Detection |
| ZHANG Hong-tao, ZHAO Xin-tao, TAN Lian, WANG Long-jie |
School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011，China
|
|
|
|
|
Abstract Biological detection is a common method in biomedicine based on the change of specific biological activity of cytokines. It is mainly applied in biomedicine, agriculture and forestry, etc., and plays an important role in the study of medical pathology and the law of crop diseases. Traditional detection methods mainly make judgments by observing the reaction of test samples to different chemical reagents. Although the detection accuracy is high, problems include cumbersome operation and long detection cycle. Hyperspectral imaging technology combines optical imaging and spectral analysis, which can simultaneously obtain the image data and spectral information of the detected samples. The image data in hyperspectral images reflect the samples' external characteristics and surface texture, while the spectral information can analyze detected samples' the internal physical structure and chemical composition. Micro-hyperspectral imaging technology is a fast, nondestructive and accurate optical imaging analysis technology that combines hyperspectral imaging technology with the biological microscope to analyze the detected samples by observing the microscopic world's image data and spectral information. In recent years, because of its high resolution and continuous data, microscopic hyperspectral imaging has attracted more and more attention in biological detection and has become one of the important means of biological detection. Based on the basic principle of spectral imaging, data processing and application of biological detection, this paper reviews the research status of microscopic hyperspectral imaging technology in biological detection in recent ten years. It puts forward some problems existing in the research process of microscopic hyperspectral imaging technology based on summarizing the research achievements. The future development trend of microhyperspectral imaging in biological detection prospects to provide a reference for the research and application of microhyperspectral imaging in biological detection.
|
|
Received: 2022-04-11
Accepted: 2022-09-12
|
|
|
|
[1] HUANG Jin, LIANG Tao-bo, XU Heng-yi（黄 瑾, 梁涛波, 许恒毅）. Chinese Bulletin of Life Sciences（生命科学）, 2021, 33(2): 255.
[2] XU Shi-ying, SUN Jia-di, ZHANG Yin-zhi, et al（徐诗荧, 孙嘉笛, 张银志, 等）. Food Research and Development（食品研究与开发）, 2021, 42(8): 160.
[3] Chen T, Zeng R, Guo W, et al. Sensors, 2018, 18(9): 2798.
[4] Pandey Piyush, Ge Yufeng, Stoerger Vincent, et al. Frontiers in Plant Science, 2017, 8(3): 1348.
[5] Adäo Telmo, Hruska Jonas, Pádua Luis, et al. Remote Sensing, 2017, 9(11): 1110.
[6] Knight Edward, Kvaran Geir. Remote Sensing, 2014, 6(11): 10286.
[7] HOU Bang-huan, YAO Min-li, WANG Rong, et al（侯榜焕, 姚敏立, 王 榕, 等）. Acta Optica Sinica（光学学报）, 2017, 37(7): 314.
[8] XIAO Gong-hai, SHU Rong, XUE Yong-qi（肖功海, 舒 嵘, 薛永祺）. Optics and Precision Engineering(光学精密工程), 2004, 12(4): 367.
[9] LIU Jia-qing, HAN Shun-li, LIU Lei, et al（刘加庆, 韩顺利, 刘 磊, 等）. Infrared（红外）, 2017, 38(7): 22.
[10] Zhou P, Zhao H, Jin S, et al. Optics Communications, 2016, 367(1): 192.
[11] ZHENG Yu-quan, YU Bing-xi（郑玉权, 禹秉熙）. National Remote Sensing Bulletin（遥感学报）, 2002，(1): 75.
[12] Goetz A F H. Remote Sensing of Environment, 2007, 113: S5.
[13] Huebschman Michael L, Schultz Roger A, Garner Harold R. IEEE Engineering in Medicine and Biology Magazine, 2002, 21(4): 104.
[14] Van Benthem Mark H, Keenan M R, Davis R, et al. Journal of Chemometrics, 2008, 22(9): 491.
[15] FAN Shi-fu, LI Yun, ZHAO You-quan（范世福, 李 昀, 赵友全）. Life Science Instruments（生命科学仪器）, 2004，(4): 24.
[16] WU Yong-qing, LI Ming, ZHANG Bo, et al（吴永清, 李 明, 张 波, 等）. Journal of The Chinese Cereals and Oils Association（中国粮油学报）, 2021, 36(5): 165.
[17] ZHOU Zhao-lu, LI Jie, HUANG Sheng-quan, et al（周昭露, 李 杰, 黄生权, 等）. Chemical Industry and Engineering Progess（化工进展）, 2016, 35(6): 1627.
[18] SHEN Guang-hui, CAO Yao-yao, LIU Xin, et al（沈广辉, 曹瑶瑶, 刘 馨, 等）. Jiangsu Journal of Agricultural Sciences（江苏农业学报）, 2021, 37(2): 509.
[19] LIU Teng-fei, LIU Wei（刘腾飞, 刘 威）. Electronic Design Engineering（电子设计工程）, 2021, 9(6): 1.
[20] LIU Yao, LI Zi-nan, WU Tao, et al（刘 瑶, 李梓楠, 吴 涛, 等）. Soybean Science（大豆科学）, 2018, 37(4): 596.
[21] ZHANG Ting-ting, XIANG Ying-ying, YANG Li-ming, et al（张婷婷, 向莹莹, 杨丽明, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 39(5): 1556.
[22] Akbari Hamed, Halig Luma V, et al. Proc SPIE, 2012, 8317: 831711.
[23] Akbari Hamed, Uto Kuniaki, Kosugi Y, et al. Cancer Science, 2011, 102(4): 852.
[24] Akbari H, Kosugi Y, Kojima K, et al. IEEE Transactions on Biomedical Engineering, 2010, 57(8): 2011.
[25] Fabelo Himar, Ortega Samuel, Ravi Daniele, et al. PLOS ONE, 2018, 13(3): e0193721.
[26] Zheludev V, Pölönen I, Neittaanmki-Perttu N, et al. Biomedical Signal Processing and Control, 2015, 16(1): 48.
[27] Kiyotoki S, Nishikawa J, Okamoto T, et al. Journal of Biomedical Optics, 2013, 18(2): 26011.
[28] Giannoni Luca, Lange Frédéric, Tachtsidis Ilias. Journal of Optics, 2018, 20(4): 044009.
[29] FANG Juan, LIU Hong-ying, CHEN Zeng-gan, et al（房 娟, 刘洪英, 陈增淦, 等）. Photographic Science and Photochemistry（影像科学与光化学）, 2015, 33(3): 203.
[30] Li Qingli, Wang Yiting, Liu Hongying, et al. Computerized Medical Imaging and Graphics, 2014, 38(3): 171.
[31] Li Qingli, Li Chang, Liu Hongying, et al. Optics & Laser Technology, 2015, 74: 79.
[32] YU Cui-rong, WANG Tian-ran, LI Sheng, et al（于翠荣, 王天然, 李 胜, 等）. Science Technology and Engineering（科学技术与工程）, 2015, 15(27): 106.
[33] Li X, Li W, Xu X D, et al. Cell Classification Using Convolutional Neural Networks in Medical Hyperspectral Imagery, 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, 2017. doi: 10.1109/ICIVC.2017.7984606.
[34] Wang Q, Wang J B, Zhou M, et al. Biomedical Optics Express, 2017, 8(6): 3017.
[35] Li Q, Liu H, Wang Y, et al. Optics & Laser Technology, 2014, 64: 337.
|
| [1] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [5] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [6] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [7] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [8] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [9] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [10] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [11] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
| [12] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [13] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [14] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
| [15] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
|
|
|
|
