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纸质出版日期： 2021-08-16 ，
录用日期： 2021-06-08
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李伟, 吕蒙, 陈天虹, 楚照耀, 陶然. 高光谱图像在生物医学中的应用[J]. 中国图象图形学报, 2021,26(8):1764-1785.
Wei Li, Meng Lyu, Tianhong Chen, Zhaoyao Chu, Ran Tao. Application of a hyperspectral image in medical field: a review[J]. Journal of Image and Graphics, 2021,26(8):1764-1785.
高光谱成像(hyperspectral imaging，HSI)作为生物医学可视化的一种新兴技术，在生物医学领域的研究正逐渐受到关注。随着高光谱成像技术以及精准医学的迅速发展，将高光谱成像技术应用于近距离的医学诊断成为新的研究趋势。高光谱成像技术能同时获取生物组织的2维空间信息和1维光谱信息，覆盖可见光、红外和紫外等光谱范围，具有较高的光谱分辨率，可提供有关组织生理、形态和生化成分的诊断信息，为生物组织学研究提供更精细的光谱特征，进而为医学病理诊断提供更多辅助信息。本文介绍了高光谱成像技术的基本原理、高光谱显微成像系统的基本构成及特点。基于此，总结并阐述了高光谱成像技术在疾病诊断和手术指导中的应用进展，涉及其在癌症、心脏病、视网膜疾病、糖尿病足、休克、组织病理学和图像引导手术等方面的应用。综合分析了高光谱成像技术在生物医学领域应用的局限性，并提出了生物医学研究领域中该技术的未来发展方向。
Hyperspectral imaging (HSI)
also known as imaging spectrometer
originated from remote sensing and has been explored for various applications. This tool has been applied in many fields
such as archaeology and art protection
vegetation and water resources control
food quality and safety control
forensics
crime scene detection
and biomedicine
owing to its advantages in acquiring 2D images in a wide range of electromagnetic spectrum. These applications mainly cover the ultraviolet (UV)
visible (VIS)
and near-infrared (near-IR or NIR) regions. HSI acquires a 3D dataset called hypercube
with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology
morphology
and composition. Furthermore
HSI can be easily adapted to other conventional techniques
such as microscopy and fundus camera. As an emerging imaging technology
HSI has been explored in a variety of laboratory experiments and clinical trials
which strongly indicates that HSI has a great potential for improving accuracy and reliability in disease detection
diagnosis
monitoring
and image-guided surgeries. In the past two decades
the HSI technology has been rapidly developed in hardware and systems. Most medical HSIs only detect the UV
VIS
and near-IR regions of light. Therefore
the exploration of HSI in the mid-IR spectrum may bring new insights for disease detection
diagnosis
and monitoring. The HSI technology is also combined with other imaging methods
such as preoperative positron emission tomography and intraoperative ultrasound
to overcome the limitation on the penetration of biological tissues and broaden HSI application areas. With the increasing integration of technologies
such as microscopes
colposcopy
laparoscopy
and fundus cameras
HSI is becoming an important part of medical imaging technology
which provides important information for potential clinical applications at the molecular
cell
tissue
and organ level. The clinical application of HSI is clearly in adolescence
and more verification is needed before it can be safely and effectively used in clinical practice. With the development of hardware technology
image analysis methods
and computing capabilities
HSI is used for the diagnosis and monitoring of non-invasive diseases
the identification and quantitative analysis of cancer biomarkers
image-guided minimally invasive surgery
and targeted drug delivery. However
HSI
as an emerging technology
also has certain limitations. At present
the application of hyperspectral detection technology in the medical field is still in the experimental stage. Useful information must be extracted from the large amount of data contained in each medical HSI. Data calibration and correction
data compression
dimensionality reduction
and analysis of data to determine the final results require a certain amount of time
which are also major challenges in the biomedical field. Higher spectral resolution
spatial resolution
and larger spectral database provide substantial spatial and spectral information. Accordingly
the main research topics in the future are the manner by which to quickly collect images of target objects in real time in a short period
effectively integrate spectroscopic instruments and algorithms
accurately diagnose the results
and combine with other imaging methods for fusion data analysis. The HSI is widely used and plays a greater role in the field of biomedicine owing to its continuous development and improvement. This work provides a comprehensive overview of HSI technologies and its medical applications
such as applications in cancer
heart disease
retinopathy
diabetic foot
shock
histopathology
and image-guided surgery. Moreover
this work presents an overview of the literature on the medical HSI technology and its applications. This work reviews the basic principles
structure
and characteristics of HSI system and elaborates the application progress of HSI in disease diagnosis and surgical guidance in recent years and analyzes the limitations of HSI and its future development direction.
医学高光谱成像精准医学医学高光谱图像分析疾病诊断手术图像指导
medical hyperspectral imageprecision medicinemedicine hyperspectral image analysisdisease diagnosisimage-guided surgery
Akbari H, Kosugi Y, Kojima K and Tanaka N. 2010. Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging. IEEE Transactions on Biomedical Engineering, 57(8): 2011-2017[DOI: 10.1109/TBME.2010.2049110]
Akbari H, Uto K, Kosugi Y, Kojima K and Tanaka N. 2011. Cancer detection using infrared hyperspectral imaging. Cancer Science, 102(4): 852-857[DOI: 10.1111/j.1349-7006.2011.01849.x]
Best S L, Thapa A, Jackson N, Olweny E, Holzer M, Park S, Wehner E, Zuzak K and Cadeddu J A. 2013. Renal oxygenation measurement during partial nephrectomy using hyperspectral imaging may predict acute postoperative renal function. Journal of Endourology, 27(8): 1037-1040[DOI: 10.1089/end.2012.0683]
Blanco F, Lopez-Mesas M, Valiente M, Serranti S, Bonifazi G and Havel J. 2012. Hyperspectral imaging based method for fast characterization of kidney stone types. Journal of Biomedical Optics, 17(7): #076027[DOI: 10.1117/1.JBO.17.7.076027]
Carrión-Camacho M R, Marín-León I, Molina-Doñoro J M and González-López J R. 2019. Safety of permanent pacemaker implantation: a prospective study. Journal of Clinical Meidicine, 8(1): #35[DOI: 10.3390/jcm8010035]
Chin J A, Wang E C and Kibbe M R. 2011. Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease. Journal of Vascular Surgery, 54(6): 1679-1688[DOI: 10.1016/j.jvs.2011.06.022]
Cui Q, Park J, Smith R T and Gao L. 2020. Snapshot hyperspectral light field imaging using image mapping spectrometry. Optics Letters, 45(3): 772-775[DOI: 10.1364/OL.382088]
Demos S G and Ramsamooj R. 2003. Hyperspectral imaging of cells: toward real-time pathological assessment//Proceedings Volume 5201, Photonic Devices and Algorithms for Computing V. San Diego, USA: SPIE: 133-137[DOI:10.1117/12.508097http://dx.doi.org/10.1117/12.508097]
Fabelo H, Ortega S, Ravi D, Kiran B R, Sosa C, Bulters D, Callicó G M, Bulstrode H, Szolna A, Piñeiro J F, Kabwama S, Madroñal D, Lazcano R, J-O'Shanahan A, Bisshopp S, Hernandez M, Baez A, Yang G Z, Stanciulescu B, Salvador R, Juarez E and Sarmiento R. 2018. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS One, 13(3): #e0193721[DOI: 10.1371/journal.pone.0193721]
Galeano J, Jolivot R and Marzani F. 2011. Analysis of human skin hyper-spectral images by non-negative matrix factorization//Proceedings of MICAI 2011: Advances in Soft Computing. Puebla, Mexico: Springer: 431-442[DOI:10.1007/978-3-642-25330-0_38http://dx.doi.org/10.1007/978-3-642-25330-0_38]
Gibbs-Strauss S L, Nasr K A, Fish K M, Khullar O, Ashitate Y, Siclovan T M, Johnson B F, Barnhardt N E, Hehir C A T and Frangioni J V. 2011. Nerve-highlighting fluorescent contrast agents for image-guided surgery. Molecular Imaging, 10(2): 91-101[DOI: 10.2310/7290.2010.00026]
Goto A, Nishikawa J, Kiyotoki S, Nakamura M, Nishimura J, Okamoto T, Ogihara H, Fujita Y, Hamamoto Y and Sakaida I. 2015. Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer. Journal of Biomedical Optics, 20(1): #016017[DOI: 10.1117/1.JBO.20.1.016017]
Gu Y Y, Partridge Y P and Zhou J. 2018. A hyperspectral dermoscopy dataset for melanoma detection//Proceedings of International Workshop on OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. Granada, Spain: Springer: 268-276[DOI:10.1007/978-3-030-01201-4_29http://dx.doi.org/10.1007/978-3-030-01201-4_29]
Huang Y. 2018. The Research on the Identification of Skin Melanoma Based on Microscopic Hyperspectral Imaging. Shanghai: East China Normal University
黄怡. 2018. 基于显微高光谱成像的皮肤黑色素瘤识别方法研究. 上海: 华东师范大学
Hu B L, Du J, Zhang Z F and Wang Q. 2019. Tumor tissue classification based on micro-hyperspectral technology and deep learning. Biomedical Optics Express, 10(12): 6370-6389[DOI: 10.1364/BOE.10.006370]
Jeyaraj P R and Nadar E R S. 2019. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. Journal of Cancer Research and Clinical Oncology, 145(4): 829-837[DOI: 10.1007/s00432-018-02834-7]
Johnson W R, Wilson D W, Fink W, Humayun M S and Bearman G H. 2007. Snapshot hyperspectral imaging in ophthalmology. Journal of Biomedical Optics, 12(1): #014036[DOI: 10.1117/1.2434950]
Khaodhiar L, Dinh T, Schomacker K T, Panasyuk S V, Freeman J E, Lew R, Vo T, Panasyuk A A, Lima C, Giurini J M, Lyons T E and Veves A. 2007. The use of medical hyperspectral technology to evaluate microcirculatory changes in diabetic foot ulcers and to predict clinical outcomes. Diabetes Care, 30(4): 903-910[DOI: 10.2337/dc06-2209]
Khoobehi B, Beach J M and Kawano H. 2004. Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head. Investigative Ophthalmology and Visual Science, 45(5): 1464-1472[DOI: 10.1167/iovs.03-1069]
Köhler H, Kulcke A, Maktabi M, Moulla Y, Jansen-Winkeln B, Barberio M, Diana M, Gockel I, Neumuth T and Chalopin C. 2020. Laparoscopic system for simultaneous high-resolution video and rapid hyperspectral imaging in the visible and near-infrared spectral range. Journal of Biomedical Optics, 25(8): #086004[DOI: 10.1117/1.JBO.25.8.086004]
Kumar K A, Thapa N and Kuriakose S A. 2015. Advances in spaceborne hyperspectral imaging systems. Current Science, 108(5): 826-832
Larsen E, Randeberg L L, Olstad E, Haugen O A, Aksnes A and Svaasand L O. 2011. Hyperspectral imaging of atherosclerotic plaques in vitro. Journal of Biomedical Optics, 16(2): #026011[DOI: 10.1117/1.3540657]
Leon R, Martinez-Vega B, Fabelo H, Ortega S, Melian V, Castaño I, Carretero G, Almeida P, Garcia A, Quevedo E, Hernandez J A, Clavo B and Callico G M. 2020. Non-Invasive skin cancer diagnosis using hyperspectral imaging for in-situ clinical support. Journal of Clinical Medicine, 9(6): #1662[DOI: 10.3390/jcm9061662]
Li Q L, Xue Y Q, Wang J Y and Yue X Q. 2006. Application of hyperspectral imaging system in tongue analysis of traditional Chinese medicine. Journal of Infrared and Millimeter Waves, 25(6): 465-468
李庆利, 薛永祺, 王建宇, 岳小强. 2006. 高光谱成像系统在中医舌诊中的应用研究. 红外与毫米波学报, 25(6): 465-468[DOI: 10.3321/j.issn:1001-9014.2006.06.016]
Li Q L, Zhang J F, Wang Y T and Xu G T. 2009. Molecular spectral imaging system for quantitative immunohistochemical analysis of early diabetic retinopathy. Applied Spectroscopy, 63(12): 1336-1342[DOI: 10.1366/000370209790108897]
Liu Z, Li Q L, Yan J Q and Tang Q L. 2007. A novel hyperspectral medical sensor for tongue diagnosis. Sensor Review, 27(1): 57-60[DOI: 10.1108/02602280710723497]
Liu Z, Wang H J and Li Q L. 2012. Tongue tumor detection in medical hyperspectral images. Sensors, 12(1): 162-174[DOI: 10.3390/s120100162]
Lu G L and Fei B W. 2014. Medical hyperspectral imaging: a review. Journal of Biomedical Optics, 19(1): #010901[DOI: 10.1117/1.JBO.19.1.010901]
Lu G L, Wang D S, Qin X L, Halig L, Muller S, Zhang H Z, Chen A, Pogue B W, Chen Z G and Fei B W. 2015. Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery. Journal of Biomedical Optics, 20(12): #126012[DOI: 10.1117/1.JBO.20.12.126012]
Lue N, Kang J W, Yu C C, Barman I, Dingari N C, Feld M S, Dasari R R and Fitzmaurice M. 2012. Portable optical fiber probe-based spectroscopic scanner for rapid cancer diagnosis: a new tool for intraoperative margin assessment. PLoS One, 7(1): #30887[DOI: 10.1371/journal.pone.0030887]
Lyu M, Li W, Tao R and Lovell N H. 2021a. Spatial-spectral density peaks-based discriminant analysis for membranous nephropathy classification using microscopic hyperspectral images. IEEE Journal of Biomedical and Health Informatics: #33434138[DOI: 10.1109/JBHI.2021.3050483]
Lyu M, Li W, Chen T H, Zhou J and Tao R. 2021b. Discriminant tensor-based manifold embedding for medical hyperspectral imagery. IEEE Journal of Biomedical and Health Informatics: #33687854[DOI: 10.1109/JBHI.2021.3065050]
Martin F L, Kelly J G, Llabjani V, Martin-Hirsch P L, Patel I I, Trevisan J, Fullwood N J and Walsh M J. 2010. Distinguishing cell types or populations based on the computational analysis of their infrared spectra. Nature Protocols, 5(11): 1748-1760[DOI: 10.1038/nprot.2010.133]
Milanic M, Paluchowski L A and Randeberg L L. 2015. Hyperspectral imaging for detection of arthritis: feasibility and prospects. Journal of Biomedical Optics, 20(9): #096011[DOI: 10.1117/1.JBO.20.9.096011]
Mitra K, Melvin J, Chang S F, Xu R, Park K, Yilmaz A and Melvin S. 2012. Indocyanine-green-loaded microballoons for biliary imaging in cholecystectomy. Journal of Biomedical Optics, 17(11): #116025[DOI: 10.1117/1.JBO.17.11.116025]
Nagaoka T, Nakamura A, Kiyohara Y and Sota T. 2012a. Melanoma screening system using hyperspectral imager attached to imaging fiberscope//Proceedings of 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego, USA: IEEE: 3728-3731[DOI:10.1109/EMBC.2012.6346777http://dx.doi.org/10.1109/EMBC.2012.6346777]
Nagaoka T, Nakamura A, Okutani H, Kiyohara Y and Sota T. 2012b. A possible melanoma discrimination index based on hyperspectral data: a pilot study. Skin Research and Technology, 18(3): 301-310[DOI: 10.1111/j.1600-0846.2011.00571.x]
Nagaoka T, Nakamura A, Okutani H, Kiyohara Y, KogaH, Saida T and Sota T. 2013. Hyperspectroscopic screening of melanoma on acral volar skin. Skin Research and Technology, 19(1): 290-296[DOI: 10.1111/j.1600-0846.2012.00642.x]
Neittaanmäki-Perttu N, Grönroos M, Tani T, Pölönen I, Ranki A, Saksela O and Snellman E. 2013. Detecting field cancerization using a hyperspectral imaging system. Lasers in Surgery and Medicine, 45(7): 410-417[DOI: 10.1002/lsm.22160]
Nunez A S, Mendenhall M J and Gross K. 2009. Melanosome level estimation in human skin from hyperspectral imagery//Proceedings of the 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Grenoble, France: IEEE: 1-4[DOI:10.1109/WHISPERS.2009.5289039http://dx.doi.org/10.1109/WHISPERS.2009.5289039]
Ogihara H, Hamamoto Y, Fujita Y, Goto A, Nishikawa J and Sakaida I. 2016. Development of a gastric cancer diagnostic support system with a pattern recognition method using a hyperspectral camera. Journal of Sensors, 2016: #1803501[DOI: 10.1155/2016/1803501]
Okimoto G S, Parker M F, Mooradian G C, Saggese S J, Grisanti A A, O'Connor D M and Miyazawa K. 2001. New features for detecting cervical precancer using hyperspectral diagnostic imaging//Proceedings Volume 4255, Clinical Diagnostic Systems. San Jose, USA: SPIE: 67-79[DOI:10.1117/12.426747http://dx.doi.org/10.1117/12.426747]
Olweny E O, Faddegon S, Best S L, Jackson N, Wehner E F, Tan Y K, Zuzak K J and Cadeddu J A. 2013. Renal oxygenation during robot-assisted laparoscopic partial nephrectomy: characterization using laparoscopic digital light processing hyperspectral imaging. Journal of Endourology, 27(3): 265-269[DOI: 10.1089/end.2012.0207]
Orellana-Rios J, Yokoyama S, Bhuiyan A, Gao L, Otero-Marquez O and Smith R T. 2020. Translational retinal imaging. Asia-Pacific Journal of Ophthalmology, 9(3): 269-277[DOI: 10.1097/APO.0000000000000292]
Panasyuk S V, Yang S, Faller D V, Ngo D, Lew R A, Freeman J E and Rogers A E. 2007. Medical hyperspectral imaging to facilitate residual tumor identification during surgery. Cancer Biology and Therapy, 6(3): 439-446[DOI: 10.4161/cbt.6.3.4018]
Randeberg L L, Larsen E L P and Svaasand L O. 2010. Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory. Journal of Biophotonics, 3(1/2): 53-65[DOI: 10.1002/jbio.200910059]
Schols R M, Alic L, Beets G L, Breukink S O, Wieringa F P and Stassen L P S. 2015. Automated spectroscopic tissue classification in colorectal surgery. Surgical Innovation, 22(6): 557-567[DOI: 10.1177/1553350615569076]
Schweizer J, Hollmach J, Steiner G, Knels L, Funk R H W and Koch E. 2012. Hyperspectral imaging-a new modality for eye diagnostics. Biomedical Engineering/Biomedizinische Technik, 57: 293-296[DOI: 10.1515/bmt-2012-4375]
Shah S A, Bachrach N, Spear S J, Letbetter D S, Stone R A, Dhir R, Prichard J W, Brown H G and LaFramboise W A. 2003. Cutaneous wound analysis using hyperspectral imaging. BioTechniques, 34(2): 408-413[DOI: 10.2144/03342pf01]
Stamatas G N, Balas C J and Kollias N. 2003. Hyperspectral image acquisition and analysis of skin//Proceedings Volume 4959, Spectral Imaging: Instrumentation, Applications, and Analysis Ⅱ. San Jose, USA: SPIE: 77-82[DOI:10.1117/12.479491http://dx.doi.org/10.1117/12.479491]
Suhalim J L, Chung C Y, Lilledahl M B, Lim R S, Levi M, Tromberg B J and Potma E O. 2012. Characterization of cholesterol crystals in atherosclerotic plaques using stimulated Raman scattering and second-harmonic generation microscopy. Biophysical Journal, 102(8): 1988-1995[DOI: 10.1016/j.bpj.2012.03.016]
Tracy C R, Terrell J D, Francis R P, Wehner E F, Smith J, Litorja M, Hawkins D L, Pearle M S, Cadeddu J A and Zuzak K J. 2010. Characterization of renal ischemia using DLP®hyperspectral imaging: a pilot study comparing artery-only occlusion versus artery and vein occlusion. Journal of Endourology, 24(3): 321-325[DOI: 10.1089/end.2009.0184]
Tsumura N, Miyake Y and Imai F H. 2001. Medical vision: measurement of skin absolute spectral-reflectance-image and the application to component analysis//Proceedings of the 3rd International Conference on Multispectral Color Science. Joensuu, Finland: [s. n.]: 25-28
Usenik P, Bürmen M, Fidler A, Pernuš F and Likar B. 2012. Automated classification and visualization of healthy and diseased hard dental tissues by near-infrared hyperspectral imaging. Applied Spectroscopy, 66(9): 1067-1074[DOI: 10.1366/11-06460]
Valdes P A, Jacobs V L, Wilson B C, Leblond F, Roberts D W and Paulsen K D. 2013. System and methods for wide-field quantitative fluorescence imaging during neurosurgery. Optics Letters, 38(15): 2786-2788[DOI: 10.1364/OL.38.002786]
Vyas S, Banerjee A and Burlina P. 2013. Estimating physiological skin parameters from hyperspectral signatures. Journal of Biomedical Optics, 18(5): #057008[DOI: 10.1117/1.JBO.18.5.057008]
Vyas S, Meyerle J and Burlina P. 2015. Non-invasive estimation of skin thickness from hyperspectral imaging and validation using echography. Computers in Biology and Medicine, 57: 173-181[DOI: 10.1016/j.compbiomed.2014.12.010]
Wahabzada M, Besser M, Khosravani M, Kuska M T, Kersting K, Mahlein A K and Stürmer E. 2017. Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering. PLoS One, 12(12): #0186425[DOI: 10.1371/journal.pone.0186425]
Wang Z, Zheng W, Hsu S C Y and Huang Z W. 2016. Optical diagnosis and characterization of dental caries with polarization-resolved hyperspectral stimulated Raman scattering microscopy. Biomedical Optics Express, 7(4): 1284-1293[DOI: 10.1364/BOE.7.001284]
Wei X L, Li W, Zhang M M and Li Q L. 2019a. Medical hyperspectral image classification based on end-to-end fusion deep neural network. IEEE Transactions on Instrumentation and Measurement, 68(11): 4481-4492[DOI: 10.1109/TIM.2018.2887069]
Wei X L, Tu T Q, Zhang N R, Yang Y, Li W G and Li W. 2019b. Membranous nephropathy identification using hyperspectral microscopic images//Proceedings of Chinese Conference on Pattern Recognition and Computer Vision. Xi'an, China: Springer: 173-184[DOI:10.1007/978-3-030-31723-2_15http://dx.doi.org/10.1007/978-3-030-31723-2_15]
Xia J S, Falco N, Benediktsson J A, Du P J and Chanussot J. 2017. Hyperspectral image classification with rotation random forest via KPCA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4): 1601-1609[DOI: 10.1109/JSTARS.2016.2636877]
Xu J S, He L and Zhang S L. 2018. Major research advances in chronic kidney disease of 2017. Clinical Focus, 33(1): 32-39
徐金升, 何雷, 张胜雷. 2018. 2017年慢性肾脏病重要研究进展. 临床荟萃, 33(1): 32-39[DOI: 10.3969/j.issn.1004-583X.2018.01.005]
Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090
张兵. 2016. 高光谱图像处理与信息提取前沿. 遥感学报,20(5): 1062-1090[DOI: 10.11834/jrs.20166179]
Zhang D, Zhang J H, Wang Z and Sun M J. 2019. Tongue colour and coating prediction in traditional Chinese medicine based on visible hyperspectral imaging. IET Image Processing, 13(12): 2265-2270[DOI: 10.1049/iet-ipr.2018.5398]
Zheng W L, Wang C J, Chang S F, Zhang S W and Xu R X. 2015. Hyperspectral wide gap second derivative analysis for in vivo detection of cervical intraepithelial neoplasia. Journal of Biomedical Optics, 20(12): #121303[DOI: 10.1117/1.JBO.20.12.121303]
Zuzak K J, Schaeberle M D, Gladwin M T, Cannon R O Ⅲ and Levin I W. 2001. Noninvasive determination of spatially resolved and time-resolved tissue perfusion in humans during nitric oxide inhibition and inhalation by use of a visible-reflectance hyperspectralimaging technique. Circulation, 104(24): 2905-2910[DOI: 10.1161/hc4901.100525]
Zuzak K J, Naik S C, Alexandrakis G, Hawkins D, Behbehani K and Livingston E H. 2007. Characterization of a near-infrared laparoscopic hyperspectral imaging system for minimally invasive surgery. Analytical Chemistry, 79(12): 4709-4715[DOI: 10.1021/ac070367n]
Zuzak K J, Naik S C, Alexandrakis G, Hawkins D, Behbehani K and Livingston E. 2008. Intraoperative bile duct visualization using near-infrared hyperspectral video imaging. American Journal of Surgery, 195(4): 491-497[DOI: 10.1016/j.amjsurg.2007.05.044]
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