{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T04:00:34Z","timestamp":1782187234460,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00138-021-01262-x","type":"journal-article","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T17:11:04Z","timestamp":1637687464000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2221-6526","authenticated-orcid":false,"given":"Biswajit","family":"Jena","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gopal Krishna","family":"Nayak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8288-1010","authenticated-orcid":false,"given":"Sanjay","family":"Saxena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"1262_CR1","doi-asserted-by":"publisher","first-page":"103804","DOI":"10.1016\/j.compbiomed.2020.103804","volume":"122","author":"GS Tandel","year":"2020","unstructured":"Tandel, Gopal\u00a0S., Balestrieri, Antonella., Jujaray, Tanay., Khanna, Narender\u00a0N., Saba, Luca.,Suri, Jasjit\u00a0S.: Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Computers in Biology and Medicine, 122:103804, (2020)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"1262_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s12650-012-0153-y","volume":"16","author":"A Padma Nanthagopal","year":"2013","unstructured":"Padma Nanthagopal, A., Sukanesh Rajamony, R.: Classification of benign and malignant brain tumor ct images using wavelet texture parameters and neural network classifier. J. Vis. 16(1), 19\u201328 (2013)","journal-title":"J. Vis."},{"key":"1262_CR3","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","volume":"30","author":"S Muhammad","year":"2019","unstructured":"Muhammad, S., Salman, K., Khan, M., Wanqing, W., Amin, U., Sung\u00a0Wook, B.: Multi-grade brain tumor classification using deep cnn with extensive data augmentation. Journal of computational science 30, 174\u2013182 (2019)","journal-title":"J. Comput. Sci."},{"key":"1262_CR4","doi-asserted-by":"crossref","unstructured":"Suneetha, B., JhansiRani, A.: A survey on image processing techniques for brain tumor detection using magnetic resonance imaging. In: 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT) , pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/IGEHT.2017.8094064"},{"key":"1262_CR5","doi-asserted-by":"publisher","first-page":"101648","DOI":"10.1016\/j.bspc.2019.101648","volume":"55","author":"L Pei","year":"2020","unstructured":"Pei, L., Bakas, S., Vossough, A., Reza, S.M.S., Davatzikos, C., Iftekharuddin, K.M.: Longitudinal brain tumor segmentation prediction in mri using feature and label fusion. Biomedical Signal Processing and Control 55, 101648 (2020)","journal-title":"Biomed. Signal Process. Control"},{"key":"1262_CR6","doi-asserted-by":"publisher","first-page":"419","DOI":"10.3389\/fnhum.2017.00419","volume":"11","author":"V Scarapicchia","year":"2017","unstructured":"Scarapicchia, V., Brown, C., Mayo, C., Gawryluk, J.R.: Functional magnetic resonance imaging and functional near-infrared spectroscopy: insights from combined recording studies. Frontiers in human neuroscience 11, 419 (2017)","journal-title":"Front. Human Neurosci."},{"key":"1262_CR7","doi-asserted-by":"publisher","first-page":"165","DOI":"10.2528\/PIERB10053112","volume":"23","author":"Nikolaos P Asimakis","year":"2010","unstructured":"Nikolaos\u00a0P Asimakis, Irene\u00a0S Karanasiou, PK\u00a0Gkonis, and Nikolaos\u00a0K Uzunoglu. Theoretical analysis of a passive acoustic brain monitoring system. Progress in Electromagnetics Research, 23:165\u2013180, 2010","journal-title":"Progr. Electromagn. Res."},{"key":"1262_CR8","doi-asserted-by":"publisher","first-page":"23","DOI":"10.2528\/PIERB11011205","volume":"29","author":"CM Chaturvedi","year":"2011","unstructured":"Chaturvedi, C.M., Singh, V.P., Singh, P., Basu, P., Singaravel, M., Shukla, R.K., Dhawan, A., Pati, A.K., Gangwar, R.K., Singh, S.: 2.45 ghz (cw) microwave irradiation alters circadian organization, spatial memory, dna structure in the brain cells and blood cell counts of male mice, mus musculus. Progr. Electromagn. Res. B 29, 23\u201342 (2011)","journal-title":"Progr. Electromagn. Res. B"},{"issue":"1","key":"1262_CR9","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1002\/(SICI)1522-2594(199907)42:1<127::AID-MRM17>3.0.CO;2-O","volume":"42","author":"L Lemieux","year":"1999","unstructured":"Lemieux, L., Hagemann, G., Krakow, K., Woermann, F.G.: Fast, accurate, and reproducible automatic segmentation of the brain in t1-weighted volume mri data. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 42(1), 127\u2013135 (1999)","journal-title":"Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med."},{"issue":"6","key":"1262_CR10","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/S0895-6111(00)00037-9","volume":"24","author":"H Tang","year":"2000","unstructured":"Tang, H., Wu, E.X., Ma, Q.Y., Gallagher, D., Perera, G.M., Zhuang, T.: MRI brain image segmentation by multi-resolution edge detection and region selection. Comput. Med. Imaging Gr. 24(6), 349\u2013357 (2000)","journal-title":"Comput. Med. Imaging Gr."},{"issue":"2","key":"1262_CR11","first-page":"1054","volume":"3","author":"V Chen","year":"2009","unstructured":"Chen, V., Ruan, S.: Graph cut based segmentation of brain tumor from mri images. International Journal on Sciences and Techniques of Automatic control & computer engineering 3(2), 1054\u20131063 (2009)","journal-title":"Int. J. Sci. Tech. Autom. Control Comput. Eng."},{"issue":"1","key":"1262_CR12","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1097\/01.rmr.0000245460.82782.69","volume":"17","author":"H Jara","year":"2006","unstructured":"Jara, H., Sakai, O., Mankal, P., Irving, R.P., Norbash, A.M.: Multispectral quantitative magnetic resonance imaging of brain iron stores: a theoretical perspective. Topics in Magnetic Resonance Imaging 17(1), 19\u201330 (2006)","journal-title":"Top. Magn. Resonan. Imaging"},{"key":"1262_CR13","doi-asserted-by":"crossref","unstructured":"Kabir, Y., Dojat, M., Scherrer, B., Forbes, F., Garbay, C.: Multimodal MRI segmentation of ischemic stroke lesions. In: 2007 29th annual international conference of the IEEE engineering in medicine and biology society, pp. 1595\u20131598 (2007)","DOI":"10.1109\/IEMBS.2007.4352610"},{"key":"1262_CR14","doi-asserted-by":"crossref","unstructured":"Mishra, S.K., Deepthi, VH.: Brain image classification by the combination of different wavelet transforms and support vector machine classification. J. Am. Intell. Human Comput. 12(6), 6741\u20136749 (2021)","DOI":"10.1007\/s12652-020-02299-y"},{"key":"1262_CR15","doi-asserted-by":"crossref","unstructured":"Gumaei, A., Hassan, M.M., Rafiul Hassan, Md., Alelaiwi, A.: Ahybrid feature extractionmethod with regularized extreme learningmachine for brain tumor classification. IEEE Access 7, 36266\u201336273 (2019)","DOI":"10.1109\/ACCESS.2019.2904145"},{"key":"1262_CR16","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.bspc.2018.08.012","volume":"47","author":"Sonali Mishra","year":"2019","unstructured":"Mishra, Sonali, Majhi, Banshidhar, Sa, Pankaj Kumar: Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomed. Signal Process. Control 47, 303\u2013311 (2019)","journal-title":"Biomed. Signal Process. Control"},{"key":"1262_CR17","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.patrec.2017.10.036","volume":"139","author":"J Amin","year":"2020","unstructured":"Amin, J., Sharif, M., Yasmin, M., Fernandes, S.L.: A distinctive approach in brain tumor detection and classification using mri. Pattern Recognition Letters 139, 118\u2013127 (2020)","journal-title":"Pattern Recognit. Lett."},{"key":"1262_CR18","doi-asserted-by":"crossref","unstructured":"Bahadure, N.B., Ray, A.K., Thethi, H.P.: Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. International journal of biomedical imaging, 2017 (2017)","DOI":"10.1155\/2017\/9749108"},{"issue":"1","key":"1262_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15623\/ijret.2014.0301001","volume":"3","author":"RP Joseph","year":"2014","unstructured":"Joseph, R.P., Singh, C.S., Manikandan, M.: Brain tumor mri image segmentation and detection in image processing. International Journal of Research in Engineering and Technology 3(1), 1\u20135 (2014)","journal-title":"Int. J. Res. Eng. Technol."},{"key":"1262_CR20","unstructured":"Marco, A., Salem, A.B.M.: An automatic classification of brain tumors through MRI using support vector machine. Egy. Comp. Sci. J., 40(3), (2016)"},{"issue":"2","key":"1262_CR21","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s11760-014-0734-4","volume":"10","author":"A Ahmadvand","year":"2016","unstructured":"Ahmadvand, A., Kabiri, P.: Multispectral MRI image segmentation using Markov random field model. Signal Image Video Process 10(2), 251\u2013258 (2016)","journal-title":"Signal Image Video Process."},{"issue":"11","key":"1262_CR22","doi-asserted-by":"publisher","first-page":"3204","DOI":"10.1109\/TBME.2013.2271383","volume":"60","author":"A Islam","year":"2013","unstructured":"Islam, A., Reza, S.M.S., Iftekharuddin, K.M.: Multifractal texture estimation for detection and segmentation of brain tumors. IEEE transactions on biomedical engineering 60(11), 3204\u20133215 (2013)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1262_CR23","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.neucom.2016.09.051","volume":"219","author":"S Abbasi","year":"2017","unstructured":"Abbasi, S., Tajeripour, F.: Detection of brain tumor in 3d mri images using local binary patterns and histogram orientation gradient. Neurocomputing 219, 526\u2013535 (2017)","journal-title":"Neurocomputing"},{"key":"1262_CR24","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.procs.2016.09.407","volume":"102","author":"A I\u015f\u0131n","year":"2016","unstructured":"I\u015f\u0131n, A., Direko\u011flu, C., \u015eah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science 102, 317\u2013324 (2016)","journal-title":"Proc. Comput. Sci."},{"key":"1262_CR25","unstructured":"Ar\u0131, B., \u015eeng\u00fcr, A., Ar\u0131, A.: Local receptive fields extreme learning machine for apricot leaf recognition. In: International Conference on Artificial Intelligence and Data Processing (IDAP16), pp. 17\u201318 (2016)"},{"issue":"10","key":"1262_CR26","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, et\u00a0al, R.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"1262_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4(1), 1\u201313 (2017)","journal-title":"Sci. Data"},{"key":"1262_CR28","unstructured":"Spyridon, B., Mauricio, R., Andras, J., Stefan, B., Markus, R., Alessandro, C., Russell,\u00a0T.S., Christoph, B., Sung,\u00a0M.H., Martin, R., et\u00a0al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprintarXiv: 1811.02629 (2018)"},{"issue":"6","key":"1262_CR29","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4itk: improved n3 bias correction. IEEE transactions on medical imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"1262_CR30","first-page":"27","volume":"13","author":"IA Humied","year":"2012","unstructured":"Humied, I.A., Abou-Chadi, F.E.Z., Rashad, M.Z.: A new combined technique for automatic contrast enhancement of digital images. Egypt. Inf. J. 13(1), 27\u201337 (2012)","journal-title":"Egypt. Inf. J."},{"key":"1262_CR31","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"6","author":"RM Haralick","year":"1973","unstructured":"Haralick, R.M., Shanmugam, K., Its Hak, D.I.N.S.T.E.I.N.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610\u2013621 (1973)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"1262_CR32","unstructured":"Qurat-Ul-Ain, G.L., Kazmi, S.B., Jaffar, M.A., Mirza, A.M.: Classification and segmentation of brain tumor using texture analysis. In: Recent advances in artificial intelligence, knowledge engineering and data bases, pp. 147-155 (2010)"},{"issue":"6","key":"1262_CR33","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/0167-8655(90)90112-F","volume":"11","author":"A Chu","year":"1990","unstructured":"Chu, A., Sehgal, C.M., Greenleaf, J.F.: Use of gray value distribution of run lengths for texture analysis. Pattern Recognit. Lett. 11(6), 415\u2013419 (1990)","journal-title":"Pattern Recognit. Lett."},{"key":"1262_CR34","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.patcog.2015.07.009","volume":"51","author":"S Tian","year":"2016","unstructured":"Tian, S., Bhattacharya, U., Lu, S., Su, B., Wang, Q., Wei, X., Lu, Y., Tan, C.L.: Multilingual scene character recognition with co-occurrence of histogram of oriented gradients. Pattern Recognition 51, 125\u2013134 (2016)","journal-title":"Pattern Recognit."},{"issue":"2","key":"1262_CR35","doi-asserted-by":"publisher","first-page":"45","DOI":"10.14203\/j.inkom.420","volume":"9","author":"E Prakasa","year":"2016","unstructured":"Prakasa, E.: Texture feature extraction by using local binary pattern. INKOM J. 9(2), 45\u201348 (2016)","journal-title":"INKOM J."},{"issue":"1","key":"1262_CR36","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/S0031-3203(99)00206-X","volume":"34","author":"A Al-Janobi","year":"2001","unstructured":"Al-Janobi, A.: Performance evaluation of cross-diagonal texture matrix method of texture analysis. Pattern Recognit. 34(1), 171\u2013180 (2001)","journal-title":"Pattern Recognit."},{"issue":"8","key":"1262_CR37","first-page":"44","volume":"7","author":"D-C He","year":"2010","unstructured":"He, D.-C., Wang, L.: Simplified texture spectrum for texture analysis. Journal of Communication and Computer 7(8), 44\u201353 (2010)","journal-title":"J. Commun. Comput."},{"issue":"1","key":"1262_CR38","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/cancers11010111","volume":"11","author":"GS Tandel","year":"2019","unstructured":"Tandel, Gopal\u00a0S., Biswas, Mainak, Kakde, Omprakash\u00a0G., Tiwari, Ashish, Suri, Harman\u00a0S., Turk, Monica, Laird, John\u00a0R., Asare, Christopher\u00a0K., Ankrah, Annabel\u00a0A., Khanna, et\u00a0al, N.N.: A review on a deep learning perspective in brain cancer classification. Cancers 11(1), 111 (201+9)","journal-title":"Cancers"},{"issue":"2","key":"1262_CR39","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/JSTARS.2012.2190266","volume":"5","author":"AC Braun","year":"2012","unstructured":"Braun, A.C., Weidner, U., Hinz, S.: Classification in high-dimensional feature spaces-assessment using svm, ivm and rvm with focus on simulated enmap data. IEEE J. Sel. Topi. Appl. Earth Obs. Remote Sens. 5(2), 436\u2013443 (2012)","journal-title":"IEEE J. Sel. Topi. Appl. Earth Obs. Remote Sens."},{"issue":"2","key":"1262_CR40","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.eswa.2005.07.019","volume":"30","author":"S Tan","year":"2006","unstructured":"Tan, S.: An effective refinement strategy for KNN text classifier. Exp. Syst. Appl. 30(2), 290\u2013298 (2006)","journal-title":"Exp. Syst. Appl."},{"issue":"2","key":"1262_CR41","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/s100440200009","volume":"5","author":"Tin Kam Ho","year":"2002","unstructured":"Tin Kam Ho: A data complexity analysis of comparative advantages of decision forest constructors. Pattern Anal. Appl. 5(2), 102\u2013112 (2002)","journal-title":"Pattern Anal. Appl."},{"issue":"2","key":"1262_CR42","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s11192-013-1228-9","volume":"101","author":"R Guns","year":"2014","unstructured":"Raf Guns and Ronald Rousseau. Recommending research collaborations using link prediction and random forest classifiers. Scientometrics, 101(2), 1461\u20131473, 2014","journal-title":"Scientometrics"},{"issue":"1","key":"1262_CR43","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.inffus.2007.07.002","volume":"9","author":"NC Oza","year":"2008","unstructured":"Oza, N.C., Tumer, K.: Classifier ensembles: Select real-world applications. Information fusion 9(1), 4\u201320 (2008)","journal-title":"Inf. Fusion"},{"issue":"2","key":"1262_CR44","first-page":"130","volume":"2","author":"P Dhanalakshmi","year":"2013","unstructured":"Dhanalakshmi, P., Kanimozhi, T.: Automatic segmentation of brain tumor using k-means clustering and its area calculation. International Journal of advanced electrical and Electronics Engineering 2(2), 130\u2013134 (2013)","journal-title":"Int. J. Adv. Elect. Electron. Eng."},{"issue":"11","key":"1262_CR45","first-page":"1072","volume":"5","author":"KA Kalema","year":"2014","unstructured":"Kalema, K.A., Bukenya, F., Rose, A.A.: A review and analysis of fuzzy-c means clustering techniques. Int J Sci Eng Res 5(11), 1072\u20137 (2014)","journal-title":"Int. J. Sci. Eng. Res."},{"key":"1262_CR46","unstructured":"Raja, K.D.: Segmenting images using hybridization of k-means and fuzzy c-means algorithms. In: Introduction to data science and machine learning, IntechOpen (2019)"},{"key":"1262_CR47","doi-asserted-by":"crossref","unstructured":"Sanjay, S., Suraj, S.: Brain tumor segmentation by texture feature extraction with the parallel implementation of fuzzy c-means using CUDA on GPU. In: 2018 5th international conference on Parallel, Distributed and Grid Computing (PDGC), pp. 580\u2013585. IEEE (2018)","DOI":"10.1109\/PDGC.2018.8745726"},{"issue":"2","key":"1262_CR48","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3390\/bdcc3020027","volume":"3","author":"MS Alam","year":"2019","unstructured":"Alam, M. S., Rahman, M. M., Hossain, M. A., Islam, M. K., Ahmed, K. M., Ahmed, K. T., Singh BC, Sipon MM,: Automatic human brain tumor detection in MRI image using template-based k means and improved fuzzy c means clustering algorithm. Big Data Cognit Comput 3(2), 27 (2019)","journal-title":"Big Data Cognit. Comput."}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01262-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-021-01262-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01262-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T22:02:58Z","timestamp":1726178578000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-021-01262-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,23]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1262"],"URL":"https:\/\/doi.org\/10.1007\/s00138-021-01262-x","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,23]]},"assertion":[{"value":"9 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Yes.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Yes.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"6"}}