How to cite this WIREs title:
WIREs Comp Stat

Recent advances in hyperspectral imaging for melanoma detection

Advanced Review

Thomas Haugland Johansen, Kajsa Møllersen, Samuel Ortega, Himar Fabelo, Aday Garcia, Gustavo M. Callico, Fred Godtliebsen

Published Online: Apr 22 2019

DOI: 10.1002/wics.1465

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

Abstract Skin cancer is one of the most common types of cancer. Skin cancers are classified as nonmelanoma and melanoma, with the first type being the most frequent and the second type being the most deadly. The key to effective treatment of skin cancer is early detection. With the recent increase of computational power, the number of algorithms to detect and classify skin lesions has increased. The overall verdict on systems based on clinical and dermoscopic images captured with conventional RGB (red, green, and blue) cameras is that they do not outperform dermatologists. Computer‐based systems based on conventional RGB images seem to have reached an upper limit in their performance, while emerging technologies such as hyperspectral and multispectral imaging might possibly improve the results. These types of images can explore spectral regions beyond the human eye capabilities. Feature selection and dimensionality reduction are crucial parts of extracting salient information from this type of data. It is necessary to extend current classification methodologies to use all of the spatiospectral information, and deep learning models should be explored since they are capable of learning robust feature detectors from data. There is a lack of large, high‐quality datasets of hyperspectral skin lesion images, and there is a need for tools that can aid with monitoring the evolution of skin lesions over time. To understand the rich information contained in hyperspectral images, further research using data science and statistical methodologies, such as functional data analysis, scale‐space theory, machine learning, and so on, are essential. This article is categorized under: Applications of Computational Statistics > Health and Medical Data/Informatics

Examples of melanoma and nonmelanoma skin cancer taken in a clinical setting from six different patients. These cases represent both nonmelanoma and melanoma skin cancer. The diagnoses of the lesions based on histopathology are as follows: (1) melanoma, (2) atypical melanocytic hyperplasia, (3) squamous cell carcinoma, (4) Bowen's disease, (5) basal cell carcinoma, and (6) seborrheic keratosis

[ Normal View | Magnified View ]

Examples of the different information captured in hyperspectral images at different wavelengths. Each image represents a reflectance image at a specific wavelength. Note how certain features appear and disappear at the various wavelengths. In particular, note how the small lesion visible near the right‐most edge of the left image is almost invisible at higher wavelengths, and at higher wavelengths, smaller subregions and structures in the central lesion become visible

[ Normal View | Magnified View ]

An example of what spectral curves for hyperspectral pixels can look like. The plot on the left shows a representation of a hyperspectral reflectance image at an arbitrarily chosen wavelength. On the right, the mean reflectance values are plotted, where the colors of the curves correspond to the colored regions in the reflectance image. The mean curves are calculated based on all pixels in each region

[ Normal View | Magnified View ]

The number of publications per year that matched our search queries and were selected for review based on our inclusion criteria. From the plot, we can see that the majority of reviewed publications were published after 2011

[ Normal View | Magnified View ]

The conceptual difference between the information richness in a hyperspectral cube and an RGB image. In the hyperspectral cube, each horizontal slice represents spatial response for a discrete wavelength. For the RGB image, each slice represents spatial information across a range of wavelengths. Each of the red, green, and blue slices are calculated based on the visual light spectrum associated with each respective color

[ Normal View | Magnified View ]

The image on the left is an example of a conventional, clinical image of a pigmented skin lesion, whereas the image on the right is an example of dermoscopic image

[ Normal View | Magnified View ]

References

Abbasi,, N. R., Shaw,, H. M., Rigel,, D. S., Friedman,, R. J., McCarthy,, W. H., Osman,, I., … Polsky,, D. (2004). Early diagnosis of cutaneous melanoma. The Journal of the American Medical Association, 292(22), 2771–2776. https://doi.org/10.1001/jama.292.22.2771
Ahnlide,, I., Bjellerup,, M., Nilsson,, F., & Nielsen,, K. (2016). Validity of ABCD rule of dermoscopy in clinical practice. Acta Dermato‐Venereologica, 96(3), 367–372. https://doi.org/10.2340/00015555-2239
American Cancer Society. (2018). Cancer facts and figures 2018. Retrieved from https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2018.html
Annessi,, G., Bono,, R., Sampogna,, F., Faraggiana,, T., & Abeni,, D. (2007). Sensitivity, specificity, and diagnostic accuracy of three dermoscopic algorithmic methods in the diagnosis of doubtful melanocytic lesions. Journal of the American Academy of Dermatology, 56(5), 759–767. https://doi.org/10.1016/j.jaad.2007.01.014
Bengio,, Y., Courville,, A., & Vincent,, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. https://doi.org/10.1109/TPAMI.2013.50
Bray,, F., Ferlay,, J., Soerjomataram,, I., Siegel,, R. L., Torre,, L. A., & Jemal,, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 00(00), 1–31. https://doi.org/10.3322/caac.21492
Carrara,, M., Bono,, A., Bartoli,, C., Colombo,, A., Lualdi,, M., Moglia,, D., … Marchesini,, R. (2007). Multispectral imaging and artificial neural network: Mimicking the management decision of the clinician facing pigmented skin lesions. Physics in Medicine and Biology, 52(9), 2599–2613. https://doi.org/10.1088/0031-9155/52/9/018
Chaudhuri,, P., & Marron,, J. S. (1999). SiZer for exploration of structures in curves. Journal of the American Statistical Association, 94(447), 807–823. https://doi.org/10.1080/01621459.1999.10474186
Chaudhuri,, P., & Marron,, J. S. (2000). Scale space view of curve estimation. The Annals of Statistics, 28(2), 408–428. https://doi.org/10.1214/aos/1016218224
Chen,, Y., Zhao,, X., & Jia,, X. (2015). Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 2381–2392. https://doi.org/10.1109/JSTARS.2015.2388577
Codella,, N. C. F., Gutman,, D., Celebi,, M. E., Helba,, B., Marchetti,, M. A., Dusza,, S. W., Kalloo,, A., Liopyris,, K., Mishra,, N., Kittler,, H., & Halpern,, A. (2018). Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 168–172). Washington, DC: IEEE. https://doi.org/10.1109/ISBI.2018.8363547
Dai,, Q., Cheng,, J.‐H., Sun,, D.‐W., & Zeng,, X.‐A. (2015). Advances in feature selection methods for hyperspectral image processing in food industry applications: A review. Critical Reviews in Food Science and Nutrition, 55(10), 1368–1382. https://doi.org/10.1080/10408398.2013.871692
Donoho,, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766. https://doi.org/10.1080/10618600.2017.1384734
Elbaum,, M., Kopf,, A. W., Rabinovitz,, H. S., Langley,, R. G., Kamino,, H., Mihm,, M. C., … Wang,, S. (2001). Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study. Journal of the American Academy of Dermatology, 44(2), 207–218. https://doi.org/10.1067/mjd.2001.110395
ElMasry,, G., Wang,, N., & Vigneault,, C. (2009). Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks. Postharvest Biology and Technology, 52(1), 1–8. https://doi.org/10.1016/j.postharvbio.2008.11.008
Esteva,, A., Kuprel,, B., Novoa,, R. A., Ko,, J., Swetter,, S. M., Blau,, H. M., & Thrun,, S. (2017). Dermatologist‐level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Ferlay,, J., Soerjomataram,, I., Ervik,, M., Dikshit,, R., Eser,, S., Mathers,, C., … Bray,, F. (2013). GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide: IARC cancerbase no. 11 [internet]. Technical report, international agency for research on cancer. Retrieved from http://globocan.iarc.fr
Hinton,, G. E., & Salakhutdinov,, R. R. (2006). Reducing the dimensionality of data with neural networks. Technical Report No. 5786. https://doi.org/10.1126/science.1127647
Jet Propulsion Laboratory, California Institute of Technology. Airborne Visible InfraRed Imaging Spectrometer (AVIRIS)—Imaging Spectroscopy. Retrieved from https://aviris.jpl.nasa.gov/aviris/imaging%7B%5C_%7Dspectroscopy.html
Kazianka,, H., Leitner,, R., & Pilz,, J. (2008). Segmentation and classification of hyper‐spectral skin data. Data Analysis, Machine Learning and Applications, 31, 245–252. https://doi.org/10.1007/978-3-540-78246-9_29
Korotkov,, K., & Garcia,, R. (2012). Computerized analysis of pigmented skin lesions: A review. Artificial Intelligence in Medicine, 56(2), 69–90. https://doi.org/10.1016/j.artmed.2012.08.002
Krizhevsky,, A., Sutskever,, I., & Hinton,, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (nips 2012). https://doi.org/10.1145/3065386
Kupetsky,, E. A., & Ferris,, L. K. (2013). The diagnostic evaluation of MelaFind multi‐spectral objective computer vision system. Expert Opinion on Medical Diagnostics, 7(4), 405–411. https://doi.org/10.1517/17530059.2013.785520
Lachenal,, G., & Ozaki,, Y. (1999). Advantages of near infrared spectroscopy for the analysis of polymers and composites. Macromolecular Symposia, 141(1), 283–292. https://doi.org/10.1002/masy.19991410123
Lawrence,, K. C., Park,, B., Windham,, W. R., & Mao,, C. (2003). Calibration of a pushbroom hyperspectral imaging system for agricultural inspection. Transactions of the ASAE, 46(2), 513–521. https://doi.org/10.13031/2013.12940
Lee,, K.‐S., Cohen,, W. B., Kennedy,, R. E., Maiersperger,, T. K., & Gower,, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91(3‐4), 508–520. https://doi.org/10.1016/j.rse.2004.04.010
Li,, Q., He,, X., Wang,, Y., Liu,, H., Xu,, D., & Guo,, F. (2013). Review of spectral imaging technology in biomedical engineering: Achievements and challenges. Journal of Biomedical Optics, 18(10), 100901. https://doi.org/10.1117/1.JBO.18.10.100901
Li,, Q., Zhou,, M., Liu,, H., Wang,, Y., & Guo,, F. (2015). Red blood cell count automation using microscopic Hyperspectral imaging technology. Applied Spectroscopy, 69(12), 1372–1380. https://doi.org/10.1366/14-07766
Lihacova,, I., Bolochko,, K., Plorina,, E. V., Lange,, M., Lihachev,, A., Bliznuks,, D., & Derjabo,, A. (2018). A method for skin malformation classification by combining multispectral and skin autofluorescence imaging. In J. Popp,, V. V. Tuchin,, & F. S. Pavone, (Eds.), Biophotonics: Photonic solutions for better health care VI (Vol. 1068535, p. 113). Strasbourg, France: SPIE. https://doi.org/10.1117/12.2306203
Lorencs,, A., Sinica‐Sinavskis,, J., Jakovels,, D., & Mednieks,, I. (2016). Melanoma‐nevus discrimination based on image statistics in few spectral channels. Elektronika ir Elektrotechnika, 22(2), 66–72. https://doi.org/10.5755/j01.eie.22.2.12173
Lu,, G., & Fei,, B. (2014). Medical hyperspectral imaging: A review. Journal of Biomedical Optics, 19(1), 010901. https://doi.org/10.1117/1.JBO.19.1.010901
MacKinnon,, N., Vasefi,, F., & Farkas,, D. L. (2014). Toward in vivo diagnosis of skin cancer using multimode imaging dermoscopy: (I) clinical system development and validation. Paper presented at the Proceedings of the SPIE 8947, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XII, 89470I, (4 March 2014). https://doi.org/10.1117/12.2041818
Makantasis,, K., Karantzalos,, K., Doulamis,, A., & Doulamis,, N. (2015). Deep supervised learning for hyperspectral data classification through convolutional neural networks. In 2015 IEEE international geoscience and remote sensing symposium (IGARSS), (Vol. 2015‐November, pp. 4959–4962). IEEE. https://doi.org/10.1109/IGARSS.2015.7326945
Møllersen,, K., Kirchesch,, H., Zortea,, M., Schopf,, T. R., Hindberg,, K., & Godtliebsen,, F. (2015). Computer‐aided decision support for melanoma detection applied on melanocytic and nonmelanocytic skin lesions: A comparison of two systems based on automatic analysis of Dermoscopic images. BioMed Research International, 2015, 1–8. https://doi.org/10.1155/2015/579282
Møllersen,, K., Zortea,, M., Schopf,, T. R., Kirchesch,, H., & Godtliebsen,, F. (2017). Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images. PLoS One, 12(12), e0190112. https://doi.org/10.1371/journal.pone.0190112
Moncrieff,, M., Cotton,, S., Claridge,, E., & Hall,, P. (2002). Spectrophotometric intracutaneous analysis: A new technique for imaging pigmented skin lesions. British Journal of Dermatology, 146(3), 448–457. https://doi.org/10.1046/j.1365-2133.2002.04569.x
Monheit,, G., Cognetta,, A. B., Ferris,, L., Rabinovitz,, H., Gross,, K., Martini,, M., … Gutkowicz‐Krusin,, D. (2011). The performance of MelaFind: A prospective multicenter study. Archives of Dermatology, 147(2), 188–194. https://doi.org/10.1001/archdermatol.2010.302
Mughees,, A., Ali,, A., & Tao,, L. (2017). Hyperspectral image classification via shape‐adaptive deep learning. In 2017 IEEE international conference on image processing (ICIP), (Vol. 2017, September, pp. 375–379). IEEE. https://doi.org/10.1109/ICIP.2017.8296306
Nachbar,, F., Stolz,, W., Merkle,, T., Cognetta,, A. B., Vogt,, T., Landthaler,, M., … Plewig,, G. (1994). The ABCD rule of dermatoscopy. {H}igh prospective value in the diagnosis of doubtful melanocytic skin lesions. Journal of the American Academy of Dermatology, 30(4), 551–559. https://doi.org/10.1016/S0190-9622(94)70061-3
Nagaoka,, T., Kiyohara,, Y., Koga,, H., Nakamura,, A., Saida,, T., & Sota,, T. (2015). Modification of a melanoma discrimination index derived from hyperspectral data: A clinical trial conducted in 2 centers between March 2011 and December 2013. Skin Research and Technology, 21(3), 278–283. https://doi.org/10.1111/srt.12188
Nagaoka,, T., Nakamura,, A., Kiyohara,, Y., & Sota,, T. (2012). Melanoma screening system using hyperspectral imager attached to imaging fiberscope. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 30, 3728–3731. https://doi.org/10.1109/EMBC.2012.6346777
Nagaoka,, T., Nakamura,, A., Okutani,, H., Kiyohara,, Y., Koga,, H., Saida,, T., & Sota,, T. (2013). Hyperspectroscopic screening of melanoma on acral volar skin. Skin Research and Technology, 19(1), 290–296. https://doi.org/10.1111/j.1600-0846.2012.00642.x
Nagaoka,, T., Nakamura,, A., Okutani,, H., Kiyohara,, Y., & Sota,, T. (2012). A possible melanoma discrimination index based on hyperspectral data: A pilot study. Skin Research and Technology, 18(3), 301–310. https://doi.org/10.1111/j.1600-0846.2011.00571.x
Nakariyakul,, S., & Casasent,, D. P. (2007). Adaptive branch and bound algorithm for selecting optimal features. Pattern Recognition Letters, 28(12), 1415–1427. https://doi.org/10.1016/j.patrec.2007.02.015
Ngiam,, J., Khosla,, A., Kim,, M., Nam,, J., Lee,, H., & Ng,, A. Y. (2011). Multimodal Deep Learning. In Proceedings of the 28th international conference on machine learning (ICML‐11) (pp. 689–696) Bellevue, Washington, DC.
Oliveira,, R. B., Papa,, J. P., Pereira,, A. S., & Tavares,, J. M. R. S. (2018). Computational methods for pigmented skin lesion classification in images: Review and future trends. Neural Computing and Applications, 29(3), 613–636. https://doi.org/10.1007/s00521-016-2482-6
Ortega,, S., Fabelo,, H., Camacho,, R., de la Luz Plaza,, M., Callico,, G. M., & Sarmiento,, R. (2018). Detecting brain tumor in pathological slides using hyperspectral imaging. Biomedical Optics Express, 9(2), 818–831. https://doi.org/10.1364/BOE.9.000818
Pan,, S. J., & Yang,, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191
Patwardhan,, S. V., & Dhawan,, A. P. (2004). Multi‐spectral imaging and analysis for classification of melanoma. Conference proceedings:… Annual International Conference of the IEEE engineering in medicine and biology Society. IEEE Engineering in Medicine and Biology Society, 1, 503–506. https://doi.org/10.1109/IEMBS.2004.1403204
Patwardhan,, S. V., Dhawan,, A. P., & Relue,, P. A. (2005). Monte Carlo simulation of light‐tissue interaction: Three‐dimensional simulation for trans‐illumination‐based imaging of skin lesions. IEEE Transactions on Biomedical Engineering, 52(7), 1227–1236. https://doi.org/10.1109/TBME.2005.847546
Qi,, X., Xing,, F., Foran,, D. J., & Yang,, L. (2011). Comparative performance analysis of stained histopathology specimens using RGB and multispectral imaging. Paper presented at the Proceedings of the SPIE 7963, Medical Imaging 2011: Computer‐Aided Diagnosis, 79633B, (9 March 2011). https://doi.org/10.1117/12.878325
Quinzán,, I., Sotoca,, J. M., Latorre‐Carmona,, P., Pla,, F., García‐Sevilla,, P., & Boldó,, E. (2013). Band selection in spectral imaging for non‐invasive melanoma diagnosis. Biomedical Optics Express, 4(4), 514–519. https://doi.org/10.1364/BOE.4.000514
Rey‐Barroso,, L., Burgos‐Fernández,, F., Delpueyo,, X., Ares,, M., Royo,, S., Malvehy,, J., … Vilaseca,, M. (2018). Visible and extended near‐infrared multispectral imaging for skin cancer diagnosis. Sensors, 18(5), 1441. https://doi.org/10.3390/s18051441
Rosado,, B., Menzies,, S., Harbauer,, A., Pehamberger,, H., Wolff,, K., Binder,, M., & Kittler,, H. (2003). Accuracy of computer diagnosis of melanoma: A quantitative meta‐analysis. Archives of Dermatology, 139(3), 361–367. https://doi.org/10.1001/archderm.139.3.361
Rubins,, U., Zaharans,, J., Ļihačova,, I., & Spigulis,, J. (2014). Multispectral video‐microscope modified for skin diagnostics. Latvian Journal of Physics and Technical Sciences, 51(5), 65–70. https://doi.org/10.2478/lpts-2014-0031
Shahshahani,, B. M., & Landgrebe,, D. A. (1994). The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Transactions on Geoscience and Remote Sensing, 32(5), 1087–1095. https://doi.org/10.1109/36.312897
Smialowski,, P., Frishman,, D., & Kramer,, S. (2010). Pitfalls of supervised feature selection. Bioinformatics, 26(3), 440–443. https://doi.org/10.1093/bioinformatics/btp621
Smith,, R. B. (2012). Introduction to hyperspectral imaging. Retrieved June 26, 2018, from https://www.microimages.com/documentation/Tutorials/hyprspec.pdf
Song,, E., Grant‐Kels,, J. M., Swede,, H., D`Antonio,, J. L., Lachance,, A., Dadras,, S. S., … Rothe,, M. J. (2016). Paired comparison of the sensitivity and specificity of multispectral digital skin lesion analysis and reflectance confocal microscopy in the detection of melanoma in vivo: A cross‐sectional study. Journal of the American Academy of Dermatology, 75(6), 1187–1192.e2. https://doi.org/10.1016/j.jaad.2016.07.022
Stamnes,, J. J., Ryzhikov,, G., Biryulina,, M., Hamre,, B., Zhao,, L., & Stamnes,, K. (2017). Optical detection and monitoring of pigmented skin lesions. Biomedical Optics Express, 8(6), 2946–2964. https://doi.org/10.1364/BOE.8.002946
Suárez,, I. Q., Carmona,, P. L., García‐Sevilla,, P., Boldo,, E., Pla,, F., Jiménez,, V. G., Lozoya,, R., & de Lucía,, G. P. (2012). Non‐invasive Melanoma Diagnosis using Multispectral Imaging. In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, (January, pp. 386–393). SciTePress–Science. https://doi.org/10.5220/0003843803860393
Świtoński,, A., Michalak,, M., Josiński,, H., & Wojciechowski,, K. (2010). Detection of tumor tissue based on the multispectral imaging. In International conference on computer vision and graphics (Vol. 1732, pp. 325–333). https://doi.org/10.1007/978-3-642-15907-7_40
Taghizadeh,, M., Gowen,, A. A., & O`Donnell,, C. P. (2011). Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms. Biosystems Engineering, 108(2), 191–194. https://doi.org/10.1016/j.biosystemseng.2010.10.005
Tomatis,, S., Bono,, A., Bartoli,, C., Carrara,, M., Lualdi,, M., Tragni,, G., & Marchesini,, R. (2003). Automated melanoma detection: Multispectral imaging and neural network approach for classification. Medical Physics, 30(2), 212–221. https://doi.org/10.1118/1.1538230
Tomatis,, S., Carrara,, M., Bono,, A., Bartoli,, C., Lualdi,, M., Tragni,, G., … Marchesini,, R. (2005). Automated melanoma detection with a novel multispectral imaging system: Results of a prospective study. Physics in Medicine and Biology, 50(8), 1675–1687. https://doi.org/10.1088/0031-9155/50/8/004
Tuia,, D., Volpi,, M., Copa,, L., Kanevski,, M., & Munoz‐Mari,, J. (2011). A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics in Signal Processing, 5(3), 606–617. https://doi.org/10.1109/JSTSP.2011.2139193
Unlu,, E., Akay,, B. N., & Erdem,, C. (2014). Comparison of dermatoscopic diagnostic algorithms based on calculation: The ABCD rule of dermatoscopy, the seven‐point checklist, the three‐point checklist and the CASH algorithm in dermatoscopic evaluation of melanocytic lesions. The Journal of Dermatology, 41(7), 598–603. https://doi.org/10.1111/1346-8138.12491
Vasefi,, F., MacKinnon,, N., Saager,, R., Kelly,, K. M., Maly,, T., Booth,, N., … Farkas,, D. L. (2016). Separating melanin from hemodynamics in nevi using multimode hyperspectral dermoscopy and spatial frequency domain spectroscopy. Journal of Biomedical Optics, 21(11), 114001. https://doi.org/10.1117/1.JBO.21.11.114001
Vestergaard,, M. E., & Menzies,, S. W. (2008). Automated diagnostic instruments for cutaneous melanoma. Seminars in Cutaneous Medicine and Surgery, 27(1), 32–36.
Wang,, Q., Wang,, J., Zhou,, M., Li,, Q., & Wang,, Y. (2017). Spectral‐spatial feature‐based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology. Biomedical Optics Express, 8(6), 3017–3028. https://doi.org/10.1364/BOE.8.003017
Wang,, W., Li,, C., Tollner,, E. W., Rains,, G. C., & Gitaitis,, R. D. (2012). A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization. Computers and Electronics in Agriculture, 80, 135–144. https://doi.org/10.1016/j.compag.2011.09.003
Wu,, D., & Sun,, D.‐W. (2013). Application of visible and near infrared hyperspectral imaging for non‐invasively measuring distribution of water‐holding capacity in salmon flesh. Talanta, 116, 266–276. https://doi.org/10.1016/j.talanta.2013.05.030
Xing,, J., Bravo,, C., Jancsók,, P. T., Ramon,, H., & De Baerdemaeker,, J. (2005). Detecting bruises on ‘golden delicious’ apples using hyperspectral imaging with multiple wavebands. Biosystems Engineering, 90(1), 27–36. https://doi.org/10.1016/j.biosystemseng.2004.08.002
Yamal,, J.‐M., Zewdie,, G. A., Cox,, D. D., Neely Atkinson,, E., Cantor,, S. B., MacAulay,, C., … Follen,, M. (2012). Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia without colposcopic tissue information; a step toward automation for low resource settings. Journal of Biomedical Optics, 17(4), 047002. https://doi.org/10.1117/1.JBO.17.4.047002
Zeiler,, M. D., & Fergus,, R. (2014). Visualizing and understanding convolutional networks (pp. 818–833). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10590-1_53
Zheludev,, V., Pölönen,, I., Neittaanmäki‐Perttu,, N., & Averbuch,, A. (2015). Biomedical signal processing and control delineation of malignant skin tumors by hyperspectral imaging using diffusion maps dimensionality reduction. Biomedical Signal Processing and Control, 16, 48–60. https://doi.org/10.1016/j.bspc.2014.10.010
Zherdeva,, L. A., Bratchenko,, I. A., Myakinin,, O. O., Moryatov,, A. A., Kozlov,, S. V., & Zakharov,, V. P. (2016). In vivo hyperspectral imaging and differentiation of skin cancer. 10024:100244G. https://doi.org/10.1117/12.2246433

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts