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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Mini-Review Article

Hyperspectral Imaging: A Review and Trends towards Medical Imaging

Author(s): Shahid Karim*, Akeel Qadir, Umar Farooq, Muhammad Shakir and Asif Ali Laghari

Volume 19, Issue 5, 2023

Published on: 26 August, 2022

Article ID: e190522205031 Pages: 11

DOI: 10.2174/1573405618666220519144358

Price: $65

Abstract

Hyperspectral Imaging (HSI) is a pertinent technique to provide meaningful information about unique objects in the medical field. This paper discusses the basic principles, imaging methods, comparisons, and advances in the medical applications of HSI to accentuate the importance of HSI in the medical field. To date, there are numerous tools and methods to fix the problems, but reliable medical HSI tools and methods need to be studied. The enactment and analytical competencies of HSI for medical imaging are discussed. Specifically, the recent successes and limitations of HSI in biomedical are presented to offer the readers an insight into its current potential for medical research. Lastly, we have discussed the future challenges concerning medical applications and possible ways to overcome these limitations.

Keywords: Hyperspectral imaging, spectral imaging, biomedical, deep learning, optical imaging, infrared.

Graphical Abstract

[1]
Luo B, Zhang L. Robust autodual morphological profiles for the classification of high-resolution satellite images. IEEE Trans Geosci Remote Sens 2014; 52(2): 1451-62.
[http://dx.doi.org/10.1109/TGRS.2013.2251468]
[2]
Hou Banghuan, Yao Minli, Wang Rong, Zhang Fenggan, Dai Dingcheng. Spatial-spectral semi-supervised local discriminant analysis for hyperspectral image classification. Acta Opt Sin 2017; 37(7): 0728002.
[http://dx.doi.org/10.3788/AOS201737.0728002]
[3]
Suzuki Y, Okamoto H, Takahashi M, Kataoka T, Shibata Y. Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging. Grassl Sci 2012; 58(1): 1-7.
[http://dx.doi.org/10.1111/j.1744-697X.2011.00239.x]
[4]
Ni J, Hong H, Zhang Y, et al. Development of a non-invasive method for skin cholesterol detection: Pre-clinical assessment in atherosclerosis screening. Biomed Eng Online 2021; 20(1): 52.
[http://dx.doi.org/10.1186/s12938-021-00889-1] [PMID: 34074299]
[5]
Vejarano R, Siche R, Tesfaye W. Evaluation of biological contaminants in foods by hyperspectral imaging: A review. Int J Food Prop 2017; 20: 1264-97.
[http://dx.doi.org/10.1080/10942912.2017.1338729]
[6]
Khan A, Munir MT, Yu W, Young BR. A review towards hyperspectral imaging for real-time quality control of food products with an illustrative case study of milk powder production. Food Bioprocess Technol 2020; 13(5): 739-52.
[http://dx.doi.org/10.1007/s11947-020-02433-w]
[7]
Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A. Modern trends in hyperspectral image analysis: A review. IEEE Access 2018; 6: 14118-29.
[http://dx.doi.org/10.1109/ACCESS.2018.2812999]
[8]
Liang H. Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Appl Phys, A Mater Sci Process 2012; 106(2): 309-23.
[http://dx.doi.org/10.1007/s00339-011-6689-1]
[9]
Edelman GJ, Gaston E, van Leeuwen TG, Cullen PJ, Aalders MCG. Hyperspectral imaging for non-contact analysis of forensic traces. Forensic Sci Int 2012; 223(1-3): 28-39.
[http://dx.doi.org/10.1016/j.forsciint.2012.09.012] [PMID: 23088824]
[10]
Pallua JD, Brunner A, Zelger B, et al. New perspectives of hyperspectral imaging for clinical research. NIR News 2021; 32(3-4): 5-13.
[http://dx.doi.org/10.1177/09603360211024971]
[11]
Yoon J. Hyperspectral Imaging for Clinical Applications. Biochip J 2022; 16(1): 1-12.
[http://dx.doi.org/10.1007/s13206-021-00041-0]
[12]
Winter EM, et al. Mine detection experiments using hyperspectral sensors. Detection and Remediation Technologies for Mines and Minelike Targets IX. 2004; 5415: pp. 1035-41.
[http://dx.doi.org/10.1117/12.548087]
[13]
Farley V, Vallières A, Villemaire A, Chamberland M, Lagueux P, Giroux J. Chemical agent detection and identification with a hyperspectral imaging infrared sensor. Electro-optical remote sensing, detection, and photonic technologies and their applications. 2007; 6739: p. 673918.
[14]
Chang C-I. Real-time progressive hyperspectral image processing. Springer 2016.
[http://dx.doi.org/10.1007/978-1-4419-6187-7]
[15]
Chen Y, Chen X, Zhou J, Ji Y, Shen W. Camouflage target detection via hyperspectral imaging plus information divergence measurement. International Conference on Optoelectronics and Microelectronics Technology and Application. 10244: 102440F.
[16]
Sit M, Demiray BZ, Xiang Z, Ewing GJ, Sermet Y, Demir I. A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol 2020; 82(12): 2635-70.
[http://dx.doi.org/10.2166/wst.2020.369] [PMID: 33341760]
[17]
Lan X, Zhao E, Li ZL, Labed J, Nerry F. Deep mixture model-based land surface temperature retrieval for hyperspectral thermal IASI sensor. IEEE Access. 2020; 8: pp. 218122-30.
[http://dx.doi.org/10.1109/ACCESS.2020.3040780]
[18]
Lu B, Dao P, Liu J, He Y, Shang J. Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens (Basel) 2020; 12(16): 2659.
[http://dx.doi.org/10.3390/rs12162659]
[19]
Jia J, et al. Tradeoffs in the spatial and spectral resolution of airborne hyperspectral imaging systems: A crop identification case study. IEEE Trans Geosci Remote Sens 2021.
[20]
Pullanagari R, Kereszturi G, Yule I. Integrating airborne hyperspectral, topographic, and soil data for estimating pasture quality using recur-sive feature elimination with random forest regression. Remote Sens (Basel) 2018; 10(7): 1117.
[http://dx.doi.org/10.3390/rs10071117]
[21]
Pour AB, Zoheir B, Pradhan B, Hashim M. Editorial for the special issue: Multispectral and hyperspectral remote sensing data for mineral exploration and environmental monitoring of mined areas. Remote Sensing. Multidisciplinary Digital Publishing Institute 2021; p. 519.
[22]
Zhi L, Zhang D, Yan J, Li QL, Tang Q. Classification of hyperspectral medical tongue images for tongue diagnosis. Comput Med Imaging Graph 2007; 31(8): 672-8.
[http://dx.doi.org/10.1016/j.compmedimag.2007.07.008] [PMID: 17920813]
[23]
Li Q, Liu Z. Tongue color analysis and discrimination based on hyperspectral images. Comput Med Imaging Graph 2009; 33(3): 217-21.
[http://dx.doi.org/10.1016/j.compmedimag.2008.12.004] [PMID: 19157779]
[24]
Balas C. A novel optical imaging method for the early detection, quantitative grading, and mapping of cancerous and precancerous lesions of cervix. IEEE Trans Biomed Eng 2001; 48(1): 96-104.
[http://dx.doi.org/10.1109/10.900259] [PMID: 11235596]
[25]
Stamatas GN, Southall M, Kollias N. In vivo monitoring of cutaneous edema using spectral imaging in the visible and near infrared. J Invest Dermatol 2006; 126(8): 1753-60.
[http://dx.doi.org/10.1038/sj.jid.5700329] [PMID: 16675964]
[26]
Sorg BS, Moeller BJ, Donovan O, Cao Y, Dewhirst MW. Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development. J Biomed Opt 2005; 10(4): 044004.
[http://dx.doi.org/10.1117/1.2003369] [PMID: 16178638]
[27]
Akbari H, Kosugi Y, Kojima K, Tanaka N. Blood vessel detection and artery-vein differentiation using hyperspectral imaging Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009; pp. 1461-4.
[http://dx.doi.org/10.1109/IEMBS.2009.5332920]
[28]
Dicker DT, Lerner J, Van Belle P, et al. Differentiation of normal skin and melanoma using high resolution hyperspectral imaging. Cancer Biol Ther 2006; 5(8): 1033-8.
[http://dx.doi.org/10.4161/cbt.5.8.3261] [PMID: 16931902]
[29]
Guo N, Zeng L, Wu Q. A method based on multispectral imaging technique for white blood cell segmentation. Comput Biol Med 2007; 37(1): 70-6.
[http://dx.doi.org/10.1016/j.compbiomed.2005.10.003] [PMID: 16325166]
[30]
Wu Q, Zeng L, Zheng H, Guo N. Precise segmentation of white blood cells by using multispectral imaging analysis techniques 2008; pp. First International Conference on Intelligent Networks and Intelligent Systems. 491-.
[http://dx.doi.org/10.1109/ICINIS.2008.105]
[31]
Anselmo VJ, Reilly TH. Medical diagnosis system and method with multispectral imaging. Google Patents 1979.
[32]
Jiao L, Shang R, Liu F, Zhang W. Brain and Nature-Inspired Learning, Computation and Recognition. Elsevier 2020.
[33]
Freeman J, Downs F, Marcucci L, Lewis EN, Blume B, Rish J. Multispectral and hyperspectral imaging: applications for medical and surgical diagnostics. Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society‘ Magnificent Milestones and Emerging Opportunities in Medical Engineering’(Cat No 97CH36136). 2: 700-1.
[http://dx.doi.org/10.1109/IEMBS.1997.757727]
[34]
Mooradian G, Weiderhold M, Dabiri AE, Coyle C. Hyperspectral imaging methods and apparatus for non-invasive diagnosis of tissue for cancer. Google Patents 1998.
[35]
Parker MF, Karins JP, O’Connor DM. Hyperspectral diagnostic imaging of the cervix: initial observations Proceedings Pacific Medical Technology Symposium-PACMEDTek Transcending Time, Distance and Structural Barriers (Cat No 98EX211). 144-8.
[http://dx.doi.org/10.1109/PACMED.1998.767958]
[36]
Ferris DG, Lawhead RA, Dickman ED, et al. Multimodal hyperspectral imaging for the noninvasive diagnosis of cervical neoplasia. J Low Genit Tract Dis 2001; 5(2): 65-72.
[http://dx.doi.org/10.1097/00128360-200004020-00001] [PMID: 17043578]
[37]
Schultz RA, Nielsen T, Zavaleta JR, Ruch R, Wyatt R, Garner HR. Hyperspectral imaging: A novel approach for microscopic analysis. Cytometry 2001; 43(4): 239-47.
[http://dx.doi.org/10.1002/1097-0320(20010401)43:4<239:AID-CYTO1056>3.0.CO;2-Z] [PMID: 11260591]
[38]
Siddiqi AM, Li H, Faruque F, et al. Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells. Cancer 2008; 114(1): 13-21.
[http://dx.doi.org/10.1002/cncr.23286] [PMID: 18213691]
[39]
Subramanian H, Pradhan P, Liu Y, et al. Partial-wave microscopic spectroscopy detects subwavelength refractive index fluctuations: an application to cancer diagnosis. Opt Lett 2009; 34(4): 518-20.
[http://dx.doi.org/10.1364/OL.34.000518] [PMID: 19373360]
[40]
Akbari H, Halig LV, Schuster DM, et al. Hyperspectral imaging and quantitative analysis for prostate cancer detection. J Biomed Opt 2012; 17(7): 0760051.
[http://dx.doi.org/10.1117/1.JBO.17.7.076005] [PMID: 22894488]
[41]
Fei B, Akbari H, Halig LV. Hyperspectral imaging and spectral-spatial classification for cancer detection 2012 5th International Conference on BioMedical Engineering and Informatics 62-4.
[http://dx.doi.org/10.1109/BMEI.2012.6513047]
[42]
Lu G, Halig L, Wang D, Chen ZG, Fei B. Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imag-ing. Medical Imaging 2014. Image Processing 2014; Vol. 9034: p. 903413.
[43]
Pike R, et al. A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. Medical Imaging 2014. Image Processing 2014; Vol. 9034: p. 90341W.
[44]
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017; 19(1): 221-48.
[http://dx.doi.org/10.1146/annurev-bioeng-071516-044442] [PMID: 28301734]
[45]
Halicek M, Lu G, Little JV, et al. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt 2017; 22(6): 060503.
[http://dx.doi.org/10.1117/1.JBO.22.6.060503] [PMID: 28655055]
[46]
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science (80-). 2006; 313: pp. 504-7.
[http://dx.doi.org/10.1126/science.1127647]
[47]
Karim S, Zhang Y, Yin S, Bibi I. Auxiliary bounding box regression for object detection in optical remote sensing imagery. Sens Imaging 2021; 22(1): 5.
[http://dx.doi.org/10.1007/s11220-020-00319-x]
[48]
Karim S, Zhang Y, Yin S, Bibi I, Brohi AA. A brief review and challenges of object detection in optical remote sensing imagery. Multiagent and Grid Systems 2020; 16(3): 227-43.
[http://dx.doi.org/10.3233/MGS-200330]
[49]
Yin S, Li H, Liu D, Karim S. Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation. Multimedia Tools Appl 2020; 79(41-42): 31049-68.
[http://dx.doi.org/10.1007/s11042-020-09640-9]
[50]
Teng L, Li H, Karim S. DMCNN: A deep multiscale convolutional neural network model for medical image segmentation 2019; 2019
[http://dx.doi.org/10.1155/2019/8597606]
[51]
Liang M, Li Z, Chen T, Zeng J. Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans Comput Biol Bioinformatics 2015; 12(4): 928-37.
[http://dx.doi.org/10.1109/TCBB.2014.2377729] [PMID: 26357333]
[52]
Gao X, Lin S, Wong TY. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng 2015; 62(11): 2693-701.
[http://dx.doi.org/10.1109/TBME.2015.2444389] [PMID: 26080373]
[53]
Conway PH, Clancy C. Charting a path from comparative effectiveness funding to improved patient-centered health care. JAMA 2010; 303(10): 985-6.
[http://dx.doi.org/10.1001/jama.2010.259] [PMID: 20215615]
[54]
Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks International conference on medical image computing and computer-assisted intervention. 411-8.
[http://dx.doi.org/10.1007/978-3-642-40763-5_51]
[55]
Wulsin DF, Gupta JR, Mani R, Blanco JA, Litt B. Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. J Neural Eng 2011; 8(3): 036015.
[http://dx.doi.org/10.1088/1741-2560/8/3/036015] [PMID: 21525569]
[56]
Ithapu VK, Singh V, Okonkwo OC, Chappell RJ, Dowling NM, Johnson SC. Alzheimer’s disease neuroimaging initiative. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Alzheimers Dement 2015; 11(12): 1489-99.
[http://dx.doi.org/10.1016/j.jalz.2015.01.010] [PMID: 26093156]
[57]
Suk HI, Lee SW, Shen D, Initiative ADN. Alzheimer’s disease neuroimaging initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 2014; 101: 569-82.
[http://dx.doi.org/10.1016/j.neuroimage.2014.06.077] [PMID: 25042445]
[58]
Fan XN, Zhang SW. lncRNA-MFDL: identification of human long non-coding RNAs by fusing multiple features and using deep learning. Mol Biosyst 2015; 11(3): 892-7.
[http://dx.doi.org/10.1039/C4MB00650J] [PMID: 25588719]
[59]
Ibrahim R, Yousri NA, Ismail MA, El-Makky NM. Multi-level gene/MiRNA feature selection using deep belief nets and active learning 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014; pp. 3957-60.
[http://dx.doi.org/10.1109/EMBC.2014.6944490]
[60]
Wang D, Shang Y. Modeling physiological data with deep belief networks. Int J Inf Educ Technol 2013; 3(5): 505-11.
[PMID: 25165501]
[61]
Yang Z, Zhong S, Carass A, Ying SH, Prince JL. Deep learning for cerebellar ataxia classification and functional score regression International Workshop on Machine Learning in Medical Imaging. 68-76.
[http://dx.doi.org/10.1007/978-3-319-10581-9_9]
[62]
Liao S, Gao Y, Oto A, Shen D. Representation learning: A unified deep learning framework for automatic prostate MR segmentation International Conference on Medical image computing and computer- assisted intervention. 254-61.
[http://dx.doi.org/10.1007/978-3-642-40763-5_32]
[63]
Wang X, Yin S, Sun K, Li H, Liu J, Karim S. GKFC-CNN: Modified Gaussian kernel fuzzy C-means and convolutional neural network for apple segmentation and recognition. J Appl Sci Eng 2020; 23(3): 555-61.
[64]
Wang J, Wang Y, Tao X, et al. PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images. Fundamental Research 2021; 1(5): 631-40.
[http://dx.doi.org/10.1016/j.fmre.2021.06.013]
[65]
Bengs M, et al. Spectral-spatial recurrent-convolutional networks for In-vivo hyperspectral tumor type classification International Conference on Medical Image Computing and Computer-Assisted Intervention. 690-9.
[http://dx.doi.org/10.1007/978-3-030-59716-0_66]
[66]
Li Q, Lin J, Clancy NT, Elson DS. Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network. Int J CARS 2019; 14(6): 987-95.
[http://dx.doi.org/10.1007/s11548-019-01940-2] [PMID: 30900114]
[67]
Annala L, Neittaanmäki N, Paoli J, Zaar O, Pölönen I. Generating hyperspectral skin cancer imagery using generative adversarial neural network 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020; pp. 1600-3.
[http://dx.doi.org/10.1109/EMBC44109.2020.9176292]
[68]
Halicek M, et al. Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology. Medical Imaging 2020. Digital Pathology 2020; Vol. 11320: p. 113200U.
[http://dx.doi.org/10.1117/12.2549994]
[69]
Reddy A V N, Krishna C P. Restricted boltzmann machine supported deep belief network for brain disorder detection. PalArch’s J Archaeol Egypt/Egyptology 2020; 17: 9755-77.
[70]
Khan U, Paheding S, Elkin CP, Devabhaktuni VK. Trends in deep learning for medical hyperspectral image analysis. IEEE Access 2021; 9: 79534-48.
[http://dx.doi.org/10.1109/ACCESS.2021.3068392]
[71]
Li Q, He X, Wang Y, Liu H, Xu D, Guo F. Review of spectral imaging technology in biomedical engineering: achievements and challenges. J Biomed Opt 2013; 18(10): 100901.
[http://dx.doi.org/10.1117/1.JBO.18.10.100901] [PMID: 24114019]
[72]
Zhu S, Su K, Liu Y, et al. Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images. Biomed Opt Express 2015; 6(4): 1135-45.
[http://dx.doi.org/10.1364/BOE.6.001135] [PMID: 25909000]
[73]
Schneider A, Feussner H. Biomedical engineering in gastrointestinal surgery. Academic Press 2017.
[74]
Wang YW, Reder NP, Kang S, Glaser AK, Liu JTC. Multiplexed optical imaging of tumor-directed nanoparticles: A review of imaging sys-tems and approaches. Nanotheranostics 2017; 1(4): 369-88.
[http://dx.doi.org/10.7150/ntno.21136] [PMID: 29071200]
[75]
Halicek M, Fabelo H, Ortega S, Callico GM, Fei B. In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer. Cancers (Basel) 2019; 11(6): 756.
[http://dx.doi.org/10.3390/cancers11060756] [PMID: 31151223]
[76]
Aiazzi B, et al. Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers. Ann Geophys 2006; 49(1)
[77]
Gao L, Kester RT, Hagen N, Tkaczyk TS. Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral mi-croscopy. Opt Express 2010; 18(14): 14330-44.
[http://dx.doi.org/10.1364/OE.18.014330] [PMID: 20639917]
[78]
Roth GA, Tahiliani S, Neu-Baker NM, Brenner SA. Hyperspectral microscopy as an analytical tool for nanomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2015; 7(4): 565-79.
[http://dx.doi.org/10.1002/wnan.1330] [PMID: 25611199]
[79]
Zhang Q, Wang Y, Qiu S, Chen J, Sun L, Li Q. 3D‐PulCNN : Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN. J Biophotonics 2021; 14(12): e202100142.
[http://dx.doi.org/10.1002/jbio.202100142] [PMID: 34405557]
[80]
Di Caprio G, Schaak D, Schonbrun E. Hyperspectral fluorescence microfluidic (HFM) microscopy. Biomed Opt Express 2013; 4(8): 1486-93.
[http://dx.doi.org/10.1364/BOE.4.001486] [PMID: 24010010]
[81]
Dudley D, Duncan WM, Slaughter J. Emerging digital micromirror device (DMD) applications. MOEMS display and imaging systems. 2003; 4985: pp. 14-25.
[http://dx.doi.org/10.1117/12.480761]
[82]
Ramella-Roman JC, Saytashev I, Piccini M. A review of polarization-based imaging technologies for clinical and preclinical applications. J Opt 2020; 22(12): 123001.
[http://dx.doi.org/10.1088/2040-8986/abbf8a]
[83]
Pu H, Lin L, Sun DW. Principles of hyperspectral microscope imaging techniques and their applications in food quality and safety detection: A review. Compr Rev Food Sci Food Saf 2019; 18(4): 853-66.
[http://dx.doi.org/10.1111/1541-4337.12432] [PMID: 33337001]
[84]
Kokawa M, Yokoya N, Ashida H, et al. Visualization of gluten, starch, and butter in pie pastry by fluorescence fingerprint imaging. Food Bioprocess Technol 2015; 8(2): 409-19.
[http://dx.doi.org/10.1007/s11947-014-1410-y]
[85]
Lichtman JW, Conchello JA. Fluorescence microscopy. Nat Methods 2005; 2(12): 910-9.
[http://dx.doi.org/10.1038/nmeth817] [PMID: 16299476]
[86]
Karoui R, Blecker C. Fluorescence spectroscopy measurement for quality assessment of food systems—a review. Food Bioprocess Technol 2011; 4(3): 364-86.
[http://dx.doi.org/10.1007/s11947-010-0370-0]
[87]
Kim MS, Lefcourt AM, Chao K, Chen YR, Kim I, Chan DE. Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part I. Application of visible and near–infrared reflectance imaging. Trans ASAE 2002; 45(6): 2027.
[88]
Lee KM, Herrman TJ. Determination and prediction of fumonisin contamination in maize by surface–enhanced Raman spectroscopy (SERS). Food Bioprocess Technol 2016; 9(4): 588-603.
[http://dx.doi.org/10.1007/s11947-015-1654-1]
[89]
Lee JA, Kozikowski RT, Sorg BS. Combination of spectral and fluorescence imaging microscopy for wide-field In vivo analysis of mi-crovessel blood supply and oxygenation. Opt Lett 2013; 38(3): 332-4.
[http://dx.doi.org/10.1364/OL.38.000332] [PMID: 23381428]
[90]
Barlow AM, Slepkov AD, Ridsdale A, McGinn PJ, Stolow A. Label-free hyperspectral nonlinear optical microscopy of the biofuel microalgae Haematococcus pluvialis. Biomed Opt Express 2014; 5(10): 3391-402.
[http://dx.doi.org/10.1364/BOE.5.003391] [PMID: 25360358]
[91]
Liang R. Biomedical optical imaging technologies: Design and applications. Springer Science & Business Media 2012.
[92]
Zhang Y, Wu X, He L, et al. Applications of hyperspectral imaging in the detection and diagnosis of solid tumors. Transl Cancer Res 2020; 9(2): 1265-77.
[http://dx.doi.org/10.21037/tcr.2019.12.53] [PMID: 35117471]
[93]
Fei B. Hyperspectral imaging in medical applications. Data Handling in Science and Technology. Elsevier 2020; Vol. 32: pp. 523-65.
[94]
Calin MA, Parasca SV, Savastru D, Manea D. Hyperspectral imaging in the medical field: Present and future. Appl Spectrosc Rev 2014; 49(6): 435-47.
[http://dx.doi.org/10.1080/05704928.2013.838678]
[95]
Lu G, Fei B. Medical hyperspectral imaging: a review. J Biomed Opt 2014; 19(1): 010901.
[http://dx.doi.org/10.1117/1.JBO.19.1.010901] [PMID: 24441941]
[96]
Zonios G, Perelman LT, Backman V, et al. Diffuse reflectance spectroscopy of human adenomatous colon polyps In vivo. Appl Opt 1999; 38(31): 6628-37.
[http://dx.doi.org/10.1364/AO.38.006628] [PMID: 18324198]
[97]
Wang LV, Wu H. Biomedical optics: principles and imaging. John Wiley & Sons 2012.
[98]
Epitropou G. Multi/hyper-spectral imaging. Handbook of biomedical Optics. CRC Press 2016; pp. 151-84.
[99]
Tuchin V V. Tissue optics 2015.
[100]
Pierce MC, Schwarz RA, Bhattar VS, et al. Accuracy of in vivo multimodal optical imaging for detection of oral neoplasia. Cancer Prev Res (Phila) 2012; 5(6): 801-9.
[http://dx.doi.org/10.1158/1940-6207.CAPR-11-0555] [PMID: 22551901]
[101]
El-Rahman SA. Performance of spectral angle mapper and parallelepiped classifiers in agriculture hyperspectral image. Int J Adv Comput Sci Appl 2016; 7(5): 55-63.
[102]
Speight PM. Update on oral epithelial dysplasia and progression to cancer. Head Neck Pathol 2007; 1(1): 61-6.
[http://dx.doi.org/10.1007/s12105-007-0014-5] [PMID: 20614284]

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