{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T14:33:30Z","timestamp":1746455610585},"publisher-location":"Cham","reference-count":60,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030158866"},{"type":"electronic","value":"9783030158873"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-15887-3_16","type":"book-chapter","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T10:02:57Z","timestamp":1563530577000},"page":"359-375","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Image Processing Based Automated Glaucoma Detection Techniques and Role of De-Noising: A Technical Survey"],"prefix":"10.1007","author":[{"given":"Sima","family":"Sahu","sequence":"first","affiliation":[]},{"given":"Harsh Vikram","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Basant","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Amit Kumar","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Prabhat","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,20]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Quigley, H. A., & Broman, A. T. (2006). The number of people with glaucoma worldwide in 2010 and 2020. British Journal of Ophthalmology, 90(3), 262-267.","DOI":"10.1136\/bjo.2005.081224"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Bock, R., Meier, J., Nyul, L. G., Hornegger, J., & Michelson, G. (2010). Glaucoma risk index: automated glaucoma detection from color fundus images. Medical image analysis, 14(3), 471-481.","DOI":"10.1016\/j.media.2009.12.006"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Garcia-Feijoo, J., Mendez-Hernandez, C. De la Casa, J. M. M., Saenz-Frances, F., Sanchez-Jean, R., & Garcia-Sanchez, J. (2016). Ultrasound Biomicroscopy in Glaucoma. In Glaucoma Imaging (pp. 97-121), Springer International Publishing.","DOI":"10.1007\/978-3-319-18959-8_4"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Huang, M. L., & Chen, H. Y. (2005). Development and comparison of automated classifiers for glaucoma diagnosis using stratus optical coherence tomography. Investigative Ophthalmology and Visual Science, 46(11), 4121-4129.","DOI":"10.1167\/iovs.05-0069"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Radhakrishan, S., Goldsmith, J., Huang, D., Westphal, V., Dueker, D. K., Rollins, A. M., Izatt, J. A., & Smith, S. D. (2005). Comparison of optical coherence tomography and ultrasound biomicroscopy for detection of Narrow Anterior Chamber Angles. Archives of Ophthalmology, 123(8), 1053-1059.","DOI":"10.1001\/archopht.123.8.1053"},{"key":"16_CR6","unstructured":"Swindale, N.V., Stjepanovic, G., Chin, A., & Mikelberg, F. S. (2000). Automated analysis of normal and glaucomatous optic nerve head topography images. Investigative ophthalmology and visual science, 41(7), 1730-1742."},{"key":"16_CR7","unstructured":"Sivalingam, E. (1995). Glaucoma: an overview\u2019. Journal of ophthalmic. Nursing & technology, 15(1), 15-18."},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Budenz, D. L., Anderson, D. r., Varma, R., Schuman, J., Cantor, L., Savell, J., \u2026& Tielsch, J. (2007). Determinants of normal retinal nerve fiber layer thickness measured by stratus OCT. Ophthalmology, 114(6), 1046-1052.","DOI":"10.1016\/j.ophtha.2006.08.046"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Yu, W., Ma, Y., Zheng, L., & Liu, K. (2016). Research of Improved Adaptive Median Filter Algorithm. In Proceedings of the 2015 international conference on Electrical and Information Technologies for Rail Transportation (pp. 27-34), Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-662-49370-0_4"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, J., Duan, L., Wong, D. W. K., Tao, D., Akiba, M., & Liu, J. (2014, September). Speckle reduction in optical coherence tomography by image registration and matrix completion. In International Conference on Medical Image Computing and Computer- Assisted Intervention (pp.162-169), Springer International Publishing.","DOI":"10.1007\/978-3-319-10404-1_21"},{"key":"16_CR11","unstructured":"Benzarti, F., & Amiri, H. (2013). Speckle noise reduction in medical ultrasound images. arXiv preprint arXiv:1305.1344."},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Meier, J., Bock, R., Michelson, G., Nyul, L. G.,& Hornegger, J. (2007, August). Effects of preprocessing eye fundus images on appearance based glaucoma classification. In International Conference on Computer Analysis of Images and Patterns (pp. 165-172). Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-540-74272-2_21"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Ishikawa, H., Stein, D. M., Wollstein, G., Beaton, S., Fujimoto, J.G., & Schuman, J.S. (2005). Macular segmentation with optical coherence tomography. Investigative ophthalmology & visual science, 46(6), 2012-2017.","DOI":"10.1167\/iovs.04-0335"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Morales, S., Naranjo, V., Angulo, J., & Alcaniz, M. (2013). Automatic detection of optic disc based on PCA and mathematical morphology. IEEE transactions on medical Imaging, 32(4), 786-796.","DOI":"10.1109\/TMI.2013.2238244"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D. W. K., Tan, N. M., Tao, D., Cheng, C. Y., Aung, T., & Wong, T. Y. (2013). Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Transaction on Medical Imaging, 32(6), 1019-1032.","DOI":"10.1109\/TMI.2013.2247770"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Joshi, G. D., Sivaswami, J., & Krishnadas, S.R. (2011). Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Transaction on Medical Imaging, 30(6), 1192-1205.","DOI":"10.1109\/TMI.2011.2106509"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Wong, D.W.K., Liu, J., Lim, J. H., Jia, X., Yin, F., Li, H., & Wong, T. Y. (2008, August). Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI. In Engineering in Medicine and Biology Society, 2008. 30th Annual International Conference of the IEEE (pp. 2266\u20132269), IEEE.","DOI":"10.1109\/IEMBS.2008.4649648"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Hatanaka, Y., Noudo, A., Maramatsu, C., Sawada, A., Hara, T., Yamamoto, T., & Fujita, H. (2011, August). Automatic measurement of cup to disc ratio based on line profile analysis in retinal images. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (pp. 3387-3390), IEEE.","DOI":"10.1109\/IEMBS.2011.6090917"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Khan, F., Khan, S.A., Yasin, U.U., ul Haq, I., & Qamar, U. (2013, October). Detection of glaucoma using retinal fundus images. In Biomedical Engineering International Conference (BMEiCON), 2013 6th (pp. 1-5), IEEE.","DOI":"10.1109\/BMEiCon.2013.6687674"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Ahmad, H., Yamin, A., Shakeel, A., Gillani, S. O., & Ansari, U. (2014, April). Detection of glaucoma using retinal fundus images. In Robotics and Emerging Allied Technologies in engineering (iCREATE), 2014 International Conferences on (pp. 321-324), IEEE.","DOI":"10.1109\/iCREATE.2014.6828388"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Turpin, A., Frank, E., Hall, M., Witten, I. H., & Johnson, C. A. (2001, April). Determining progression in glaucoma using visual fields. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp.136-147), Springer, Berlin, Heidelberg.","DOI":"10.1007\/3-540-45357-1_17"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Nayak, J., Acharya, R., Bhat, P. S., Shetty, N., & Lim, T. C. (2009). Automated diagnosis of glaucoma using digital fundus images. Journals of medical systems, 33(5), 337-346.","DOI":"10.1007\/s10916-008-9195-z"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Huang, M. L., Chen, H. Y., & Huang, J. J. (2007). Glaucoma detection using adaptive neuro-fuzzy inference system. Expert systems with applications, 32(2), 458-468.","DOI":"10.1016\/j.eswa.2005.12.010"},{"key":"16_CR24","unstructured":"Bock, R., Meier, J., Michelson, G., Nyul, L., & Hornegger, J. (2007). Classifying glaucoma with image-based features from fundus photographs. Pattern Recognition, 355-364."},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Nyul, L. G. (2009, October). Retinal Image Analysis for Automated Glaucoma Risk Evaluation. In 6th International Symposium on Multispectral Image Processing and Pattern Recognition (pp. 74971C-74971C), International Society for optics and photonics.","DOI":"10.1117\/12.851179"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Ferreras, A., Pajarin, A. B., Polo, V., Larrosa, J. M., Pablo, L.E., & Honrubia, F.M. (2007). Diagnostic ability of Heidelberg Retinal Tomograph 3 Classifications: glaucoma probability score versus Moorfields regression analysis. Ophthalmology, 114 (11), 1981-1987.","DOI":"10.1016\/j.ophtha.2007.01.015"},{"key":"16_CR27","unstructured":"Atlas, L., Li, Q., & Thompson, J. (2004, May). Homomorphic modulation spectra. In Acoustics, Speech, and Signal Processing, 2004, Proceedings. (ICASSP\u201904), IEEE International Conference on (Vol. 2, pp.761-764), IEEE."},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Desjardins, A. E., Vakoc, B.J., Oh, W. Y., Motaghiannezam, S. M. R., Tearney, G. J., & Bouma, B.E. (2007). Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction. Optics Express, 15(10), 6200-6209.","DOI":"10.1364\/OE.15.006200"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Iftimia, N., Bouma, B. E., & Tearney, G. J. (2003). Speckle reduction in optical coherence tomography by path length encoded angular compounding. Journal of Biomedical Optics, 8(2), 260-263.","DOI":"10.1117\/1.1559060"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Jorgensen, T. M., Thrane, L., Mogensen, M., Pedersen, F., & Andersen, P. E. (2007, June). Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method. In European Conference on Biomedical Optics (p. 6627-22), Optical Society of America.","DOI":"10.1364\/ECBO.2007.6627_22"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Kim, J., Miller, D. T., Kim, E., Oh, S., Oh, J., & Milner, T. E. (2005). Optical Coherence Tomography Speckle Reduction by a Partially Spatially Coherent Source. Journal of Biomedical Optics, 10(6), 064034-064034.","DOI":"10.1117\/1.2138031"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Kobayashi, M., Hanafusa, H., Takada, K., & Noda, J. (1991). Polarization-independent interferometric optical-time-domain reflectometer. Journal of Lightwave Technology, 9(5), 623-628.","DOI":"10.1109\/50.79538"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Pircher, M., Go, E., Leitgeb, R., Fercher, A. F., & Hitzenberger, C. K. (2003). Speckle reduction in optical coherence tomography by frequency compounding. Journal of Biomedical Optics, 8(3), 565-569.","DOI":"10.1117\/1.1578087"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Loupas, T., McDicken, W. N., & Allan, P.L. (1989). An adaptive weighted median filter for speckle suppression in medical ultrasound images. IEEE Transactions on Circuits and Systems, 36(1), 129-135.","DOI":"10.1109\/31.16577"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Rogowska, J., & Brezinski, M. E. (2000). Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging. IEEE Transaction on Medical Imaging, 19(12), 1261-1266.","DOI":"10.1109\/42.897820"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Aja, S., Alberola, C., & Ruiz, A. (2001). Fuzzy Anisotropic diffusion for speckle filtering. In Acoustics, Speech, and Signal Processing Proceedings, 2001.Proceedings, (ICASSP\u201901), 2001 IEEE International Conference on (Vol. 2, pp.1261-1264), IEEE.","DOI":"10.1109\/ICASSP.2001.941154"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Ramos-Llorden, G., Vegas-Sanchez-Ferrero, G., Martin-Fernandez, M., Alberola-Lopez, C., & Aja-Fernandez, S. (2015). Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Transaction on Image Processing, 24(1), 345-358.","DOI":"10.1109\/TIP.2014.2371244"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Anantrasirichai, N. Nicholson, L., Morgan, J. E., Erchova, I., Mortlock, K., North, R. V., Albon, J., & Achim, A. (2014). Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography. Computerized Medical Imaging and Graphics, 38(6), 526-539.","DOI":"10.1016\/j.compmedimag.2014.06.012"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Yang, J., Fan, J., Ai, D., Wang, X., Zheng, Y., Tang, S., & Wang, Y. (2016). Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image. Neurocomputing, 195, 88-95.","DOI":"10.1016\/j.neucom.2015.05.140"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Habib, W., Sarwar, T., Siddiqui, A. M., & Touqir, I. (2016). Wavelet denoising of multiframe optical coherence tomography data using similarity measures. IET Image Processing, 11(1), 64-79.","DOI":"10.1049\/iet-ipr.2016.0160"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Gupta, A., Tripathi, A., & Bhateja, V. (2013). Despeckling of SAR images in contourlet domain using a new adaptive thresholding. In Advance Computing Conference (IACC), 2013 IEEE 3rd International (pp.1257-1261), IEEE.","DOI":"10.1109\/IAdCC.2013.6514408"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Xu, J., Ou, H., Lam, E. Y., Chui, P. C., & Wong, K. K. Y. (2013). Speckle reduction of retinal optical coherence tomography based on contourlet shrinkage. Optic Letters, 38(15), 2900-2903.","DOI":"10.1364\/OL.38.002900"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Rabbani, H., Vafadust, M., Abolmaesumi, P., & Gazor, S. (2008). Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors. IEEE Transactions on Biomedical Engineering, 55(9), 2152-2160.","DOI":"10.1109\/TBME.2008.923140"},{"key":"16_CR44","doi-asserted-by":"crossref","unstructured":"Sudeep, P.V., Niwas, S. I., Palanisamy, P., Rajan, J., Xiaojun, Y., Wang, X., Luo, Y., & Liu, L. (2016). Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering. Computers in Biology and Medicine, 71, 97-107.","DOI":"10.1016\/j.compbiomed.2016.02.003"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Sudha, S., Suresh, G. R., & Sukanesh, R. (2009). Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance. International Journal of Computer Theory and Engineering, 1(1), 1793-8201.","DOI":"10.7763\/IJCTE.2009.V1.2"},{"key":"16_CR46","doi-asserted-by":"crossref","unstructured":"Gupta, S., Chauhan, R. C., & Sexana, S. C. (2004). Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Medical and Biological Engineering and Computing, 42(2), 189-192.","DOI":"10.1007\/BF02344630"},{"key":"16_CR47","doi-asserted-by":"crossref","unstructured":"Fablet, R., Augustin, J.M., & Isar, A. (2005, June). Speckle Denoising Using a Variational Multi-wavelet Approach. In Oceans 2005-Europe (Vol. 1, pp. 539-544).IEEE.","DOI":"10.1109\/OCEANSE.2005.1511772"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Andria, G., Attivissimo, F., Lanzolla, A. M., & Savino, M. (2013). A suitable threshold for speckle reduction in ultrasound images. IEEE Transaction on Instrumentation and Measurement, 62(8), 2270-2279.","DOI":"10.1109\/TIM.2013.2255978"},{"key":"16_CR49","doi-asserted-by":"crossref","unstructured":"Bhuiyan, M. I. H., Ahmad, M. O., & Swamy, M. N. S. (2009). Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images. IET Image Processing, 3(3), 147-162.","DOI":"10.1049\/iet-ipr.2007.0096"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Bibalan, M. H., & Amindavar, H. (2016). Non-Gaussian amplitude PDF modeling of ultrasound images based on a novel generalized Cauchy-Rayleigh mixture. EURASIP Journal on Image and video Processing, 2016(1), 48.","DOI":"10.1186\/s13640-016-0148-z"},{"key":"16_CR51","unstructured":"Jafari, S., & Ghofrani, S. (2017). Using Heavy-Tailed Levy model in non subsampled shearlet transform domain for ultrasound image despeckling, Jounal of Advances in Computer Research. 8(2), 53-66."},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Fernadez, D. C., Salinas, H. M., & Puliafito, C. A. (2005). Automated detection of retinal layer structures on optical coherence tomography images. Optic Express, 13(25), 10200-10216.","DOI":"10.1364\/OPEX.13.010200"},{"key":"16_CR53","doi-asserted-by":"crossref","unstructured":"Garvin, M. K., Abramoff, M. D., Kardon, R., Russell, S. R., Wu, X., & Sonka, M. (2008). Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search. IEEE Transaction on Medical Imaging, 27(10), 1495-1505.","DOI":"10.1109\/TMI.2008.923966"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Ghafaryasl, B., Baart, R., de Boer, J. F., Van Vliet, L.J., & Vermeer, K. A. (2017, February). Automatic estimation of retinal nerve fiber bundle orientation in SD-OCT images using a structure-oriented smoothing filter. In SPIE medical Imaging (pp. 101330C-101330C). International Society for Optics and Photonics.","DOI":"10.1117\/12.2254135"},{"key":"16_CR55","doi-asserted-by":"crossref","unstructured":"Yu, Y., & Acton, S. T. (2002). Speckle Reducing Anisotropic Diffusion. IEEE Transactions on Image Processing. 11(11), 1260-1270.","DOI":"10.1109\/TIP.2002.804276"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Sahu, S., Singh, H. V., Kumar, B., & Singh, A. K. (2018). A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images. Journal of Intelligent Systems. \n                  https:\/\/doi.org\/10.1515\/jisys-2017-0402","DOI":"10.1515\/jisys-2017-0402"},{"key":"16_CR57","doi-asserted-by":"crossref","unstructured":"Sahu, S., Singh, H.V., Kumar, B. and Singh, A.K., (2018). Statistical Modeling and Gaussianization Procedure based de-speckling algorithm for Retinal OCT images, Journal of Ambient Intelligence and Humanized Computing (AIHC), 1-14.","DOI":"10.1007\/s12652-018-0823-2"},{"key":"16_CR58","doi-asserted-by":"crossref","unstructured":"Sahu, S., Singh, H. V., Kumar, B., & Singh, A. K. (2019). De-noising of ultrasound image using Bayesian approached heavy-tailed Cauchy distribution. Multimedia Tools and Applications, 78(4), 4089\u20134106.","DOI":"10.1007\/s11042-017-5221-9"},{"key":"16_CR59","doi-asserted-by":"publisher","unstructured":"Sahu, S., Singh, H.V. and Kumar, B., 2017, December. A heavy-tailed levy distribution for despeckling ultrasound image. Fourth IEEE International Conference on Image Information Processing (ICIIP), Himachal Pradesh, India, December 21-23, 2017, pp. 1-5. \n                  https:\/\/doi.org\/10.1109\/ICIIP.2017.8313674","DOI":"10.1109\/ICIIP.2017.8313674"},{"key":"16_CR60","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.optlastec.2018.06.061","volume":"110","author":"Sonali","year":"2019","unstructured":"Sonali, Sahu, S., Singh, A.K., Ghrera, S.P. and Elhoseny, M., 2018. An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Optics & Laser Technology, an International Journal of Elsevier. \n                  https:\/\/doi.org\/10.1016\/j.optlastec.2018.06.061","journal-title":"Optics & Laser Technology"}],"container-title":["Handbook of Multimedia Information Security: Techniques and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-15887-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T10:45:52Z","timestamp":1563533152000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-15887-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030158866","9783030158873"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-15887-3_16","relation":{},"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}