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| Research on Shortwave Infrared Multispectral Fluorescence Imaging of Mouse Vein |
| ZHANG Rui1, 2, 3, TANG Xin-yi1, 2, ZHU Wen-qing1, 2, 3 |
1. University of Chinese Academy of Sciences, Beijing 100049, China
2. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
3. Chinese Academy of Sciences Key Laboratory of Infrared System Detection and Imaging Technology, Beijing 100049, China
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Abstract Short-wave infrared (referred to as SWIR) generally refers to the 900~1 700 nm light band, which is invisible to the naked eye. This band’s mainstream detectors are InGaAs, which are mainly used for military, biological, biological and material spectral analysis. In the field of biological tissue observation, short-wave infrared fluorescence imaging is characterized by small optical damage to biological tissues, large imaging depth, high imaging signal-to-noise ratio, and high spatial and temporal imaging resolution, making bio-optical imaging based on InGaAs detectors biological Organize research focus in the field of observation. The bio-optical window’s multi-window and wide-spectrum fluorescence spectrum characteristics allow us to collect multi-spectrum spectral images of biological tissues to observe the structural characteristics of biological tissues under different spectral illuminations, which further facilitates scientific knowledge research. In this paper, a multi-spectral imaging system of mouse vein based on InGaAs detector was designed for the spectral characteristics of the bio-optical window, which can collect the vein images of mice without contact and help observe the infrared spectrum of mouse veins. The system based on the InGaAs detector we designed can achieve an integration time of up to 5 000 ms. By extending the integration time, the signal-to-noise ratio of vein imaging is significantly improved, and the detector spectral response characteristics cover the second bio-optical window and a third bio-optical window. From the imaging characteristics of optical microscopy and the characteristic expression of vein tissue in the image, a new single-spectrum multi-focal fusion algorithm is designed to which can well realize the infrared spectrum observation of vein images. This paper proposes a novel multi-focus fusion algorithm based on a multi-scale gradient domain guided filter (GDGF) to compensate for the imaging defects of microscopic characteristics. The multi-scale gradient domain guided filter algorithm extracts the focus pixel region, and then the fusion decision function is calculated. Finally, the fusion decision function is definedby the gradient domain guided filter algorithm, and finally, the final decision fusion function of our fusion algorithm is obtained. Experiments show that the short-wave infrared InGaAs detector designed by us well meets the requirements of fluorescence imaging of mouse veins and achieves spectral imaging of multiple bands including 1 100, 1 250 and 1 350 nm for mouse veins, as well as spectral imaging in multi-focus with the same laser illumination. Meanwhile, the fusion algorithm we designed can well extract the focusing area of the mouse vein image, which can fuse the multi-focus image and reduce the introduction of noise at the same time, thus achieving high-quality global vein imaging.
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Received: 2020-11-26
Accepted: 2021-02-24
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[1] Zhu Zhiqin, Zheng Mingyao, Qi Guanqiu, et al. IEEE Access, 2019, 7: 20811.
[2] Ma Jinlei, Zhou Zhiqiang, Wang Bo, et al. Infrared Physics & Technology, 2017, 82: 8.
[3] Song Xu, Wu Xiaojun. Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, 2019, 11377: 1.
[4] Haghighat M B A, Aghagolzadeh A, Seyedarabi H. Real-Time Fusion of Multi-Focus Images for Visual Sensor Networks. 2010 6th Iranian Conference on Machine Vision and Image Processing, 2010. 1.
[5] Mohammad Bagher Akbari Haghighat, Aghagolzadeh Ali, Seyedarabi Hadi. Computers & Electrical Engineering, 2011, 37(5): 789.
[6] Kou Fei, Chen Weihai, Wen Changyun, et al. IEEE Trans Image Process, 2015, 24(11): 4528.
[7] Qiu Xiaohua, Li Min, Zhang Liqiong, et al.Signal Processing: Image Communication, 2019, 72: 35.
[8] Liu Yu, Wang Lei, Cheng Juan, et al. Information Fusion, 2020, 64: 71.
[9] Wei Tan, Zhou Huixin, Rong Shenghui, et al. Appl. Opt., 2018, 57(35): 10092.
[10] Jian Lihua, Yang Xiaomin, Wu Wei, et al. Journal of Ambient Intelligence and Humanized Computing, 2018, https://doi.org/10.1007/s12652-018-0866-4.
[11] Chen Guo, Li Li, Jin Weiqi, et al. IEEE Photonics Journal, 2019, 11(5): 1.
[12] Zhu Jin, Jin Weiqi, Li Li, et al.Infrared Physics & Technology, 2018, 89: 8.
[13] Zhou Fuqiang, Li Xiaosong, Li Juan, et al.IEEE Access, 2019, 7: 50780.
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