{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T11:47:30Z","timestamp":1724932050327},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s00500-020-04842-7","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T06:35:31Z","timestamp":1584081331000},"page":"14885-14905","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm"],"prefix":"10.1007","volume":"24","author":[{"given":"Mohamed","family":"Abd Elaziz","sequence":"first","affiliation":[]},{"given":"Uddalok","family":"Sarkar","sequence":"additional","affiliation":[]},{"given":"Sayan","family":"Nag","sequence":"additional","affiliation":[]},{"given":"Salvador","family":"Hinojosa","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Oliva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"4842_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.swevo.2013.02.001","volume":"11","author":"S Agrawal","year":"2013","unstructured":"Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16\u201330. https:\/\/doi.org\/10.1016\/j.swevo.2013.02.001","journal-title":"Swarm Evol Comput"},{"key":"4842_CR2","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s10462-016-9486-6","volume":"47","author":"S Akyol","year":"2017","unstructured":"Akyol S, Alatas B (2017) Plant intelligence based metaheuristic optimization algorithms. Artif Intell Rev 47:417\u2013462. https:\/\/doi.org\/10.1007\/s10462-016-9486-6","journal-title":"Artif Intell Rev"},{"key":"4842_CR3","doi-asserted-by":"crossref","unstructured":"Benzid R, Arar D, Bentoumi M (2008) A fast technique for gray level image thresholding and quantization based on the entropy maximization. In: 5th international multi-conference on systems, signals and devices. IEEE, Amman, pp 1\u20134","DOI":"10.1109\/SSD.2008.4632831"},{"key":"4842_CR4","doi-asserted-by":"publisher","first-page":"8707","DOI":"10.1016\/j.eswa.2015.07.025","volume":"42","author":"AK Bhandari","year":"2015","unstructured":"Bhandari AK, Kumar A, Singh GK (2015a) Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Syst Appl 42:8707\u20138730. https:\/\/doi.org\/10.1016\/j.eswa.2015.07.025","journal-title":"Expert Syst Appl"},{"key":"4842_CR5","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1016\/j.eswa.2014.09.049","volume":"42","author":"AK Bhandari","year":"2015","unstructured":"Bhandari AK, Kumar A, Singh GK (2015b) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur\u2019s, Otsu and Tsallis functions. Expert Syst Appl 42:1573\u20131601","journal-title":"Expert Syst Appl"},{"key":"4842_CR6","doi-asserted-by":"publisher","unstructured":"Burman R, Paul S, Das S (2013) A differential evolution approach to multi-level image thresholding using type II fuzzy sets. Lecture Notes in Computer Science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNCS, vol 8297, pp 274\u2013285. https:\/\/doi.org\/10.1007\/978-3-319-03753-0_25","DOI":"10.1007\/978-3-319-03753-0_25"},{"key":"4842_CR7","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/info8030097","volume":"8","author":"O Castillo","year":"2017","unstructured":"Castillo O, Sanchez M, Gonzalez C, Martinez G (2017) Review of recent type-2 fuzzy image processing applications. Information 8:97. https:\/\/doi.org\/10.3390\/info8030097","journal-title":"Information"},{"key":"4842_CR8","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s10732-008-9080-4","volume":"15","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms\u2019 behaviour: a case study on the CEC 2005 special session on real parameter optimization. J Heuristics 15:617\u2013644. https:\/\/doi.org\/10.1007\/s10732-008-9080-4","journal-title":"J Heuristics"},{"key":"4842_CR9","volume-title":"Digital image processing","author":"RC Gonzalez","year":"1992","unstructured":"Gonzalez RC, Woods RE (1992) Digital image processing. Prentice-Hall, Upper Saddle River, NJ"},{"key":"4842_CR10","doi-asserted-by":"publisher","first-page":"14805","DOI":"10.1016\/j.eswa.2011.05.069","volume":"38","author":"M-H Horng","year":"2011","unstructured":"Horng M-H, Liou R-J (2011) Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst Appl 38:14805\u201314811. https:\/\/doi.org\/10.1016\/j.eswa.2011.05.069","journal-title":"Expert Syst Appl"},{"key":"4842_CR11","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","volume":"29","author":"JN Kapur","year":"1985","unstructured":"Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273\u2013285","journal-title":"Comput Vis Graph Image Process"},{"key":"4842_CR12","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: International conference on neural networks, 1995 proceedings, vol 4, pp 1942\u20131948. https:\/\/doi.org\/10.1109\/icnn.1995.488968","DOI":"10.1109\/icnn.1995.488968"},{"key":"4842_CR13","doi-asserted-by":"crossref","unstructured":"Kong X, Chen Y-L, Xie W, Wu X (2012) A novel paddy field algorithm based on pattern search method. In: Proceedings of the international conference on information and automation, pp 686\u2013690","DOI":"10.1109\/ICInfA.2012.6246764"},{"key":"4842_CR14","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s12293-013-0123-5","volume":"5","author":"S Kumar","year":"2013","unstructured":"Kumar S, Kumar P, Sharma TK, Pant M (2013) Bi-level thresholding using PSO, artificial bee colony and MRLDE embedded with Otsu method. Memet Comput 5:323\u2013334. https:\/\/doi.org\/10.1007\/s12293-013-0123-5","journal-title":"Memet Comput"},{"key":"4842_CR15","unstructured":"Lan S, Li LIU, Kong Z, Wang JG (2010) Segmentation approach based on fuzzy Renyi entropy"},{"key":"4842_CR16","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1016\/0031-3203(93)90115-D","volume":"26","author":"CH Li","year":"1993","unstructured":"Li CH, Lee CK (1993) Minimum cross entropy thresholding. Pattern Recognit 26:617\u2013625. https:\/\/doi.org\/10.1016\/0031-3203(93)90115-D","journal-title":"Pattern Recognit"},{"key":"4842_CR17","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/J.AMC.2014.03.152","volume":"238","author":"H Liu","year":"2014","unstructured":"Liu H, Ding G, Wang B (2014) Bare-bones particle swarm optimization with disruption operator. Appl Math Comput 238:106\u2013122. https:\/\/doi.org\/10.1016\/J.AMC.2014.03.152","journal-title":"Appl Math Comput"},{"key":"4842_CR18","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/J.NEUCOM.2017.04.053","volume":"260","author":"MM Mafarja","year":"2017","unstructured":"Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302\u2013312. https:\/\/doi.org\/10.1016\/J.NEUCOM.2017.04.053","journal-title":"Neurocomputing"},{"key":"4842_CR19","doi-asserted-by":"publisher","unstructured":"Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings eighth IEEE international conference on computer vision, ICCV 2001, vol 2, pp 416\u2013423. https:\/\/doi.org\/10.1109\/ICCV.2001.937655","DOI":"10.1109\/ICCV.2001.937655"},{"key":"4842_CR20","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.neucom.2015.05.043","volume":"168","author":"Q Miao","year":"2015","unstructured":"Miao Q, Xu P, Liu T et al (2015) A novel fast image segmentation algorithm for large topographic maps. Neurocomputing 168:808\u2013822. https:\/\/doi.org\/10.1016\/j.neucom.2015.05.043","journal-title":"Neurocomputing"},{"key":"4842_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4638-5","author":"A Mostafa","year":"2017","unstructured":"Mostafa A, Hassanien AE, Houseni M, Hefny H (2017) Liver segmentation in MRI images based on whale optimization algorithm. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-017-4638-5","journal-title":"Multimed Tools Appl"},{"key":"4842_CR22","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/575414","author":"D Oliva","year":"2013","unstructured":"Oliva D, Cuevas E, Pajares G et al (2013) Multilevel thresholding segmentation based on harmony search optimization. J Appl Math. https:\/\/doi.org\/10.1155\/2013\/575414","journal-title":"J Appl Math"},{"key":"4842_CR23","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.neucom.2014.02.020","volume":"139","author":"D Oliva","year":"2014","unstructured":"Oliva D, Cuevas E, Pajares G et al (2014) A multilevel thresholding algorithm using electromagnetism optimization. Neurocomputing 139:357\u2013381","journal-title":"Neurocomputing"},{"key":"4842_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.03.028","author":"D Oliva","year":"2015","unstructured":"Oliva D, Osuna-Enciso V, Cuevas E et al (2015) Improving segmentation velocity using an evolutionary method. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2015.03.028","journal-title":"Expert Syst Appl"},{"key":"4842_CR25","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62\u201366. https:\/\/doi.org\/10.1109\/TSMC.1979.4310076","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"4842_CR26","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.eswa.2016.02.024","volume":"55","author":"S Ouadfel","year":"2016","unstructured":"Ouadfel S, Taleb-Ahmed A (2016) Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst Appl 55:566\u2013584. https:\/\/doi.org\/10.1016\/j.eswa.2016.02.024","journal-title":"Expert Syst Appl"},{"key":"4842_CR27","doi-asserted-by":"crossref","unstructured":"Premaratne U, Samarabandu J, Sidhu T (2009) A new biologically inspired optimization algorithm. In: 2009 International conference on industrial and information systems (ICIIS). IEEE, pp 279\u2013284","DOI":"10.1109\/ICIINFS.2009.5429852"},{"key":"4842_CR28","doi-asserted-by":"publisher","unstructured":"Riomoros I, Pajares G, Herrera PJ et al (2010) Automatic image segmentation of greenness in crop fields. In: Proceedings of the 2010 international conference on soft computing and pattern recognition, SoCPaR 2010, pp 462\u2013467. https:\/\/doi.org\/10.1109\/SOCPAR.2010.5685936","DOI":"10.1109\/SOCPAR.2010.5685936"},{"key":"4842_CR29","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1016\/j.patcog.2003.10.008","volume":"37","author":"PK Sahoo","year":"2004","unstructured":"Sahoo PK, Arora G (2004) A thresholding method based on two-dimensional Renyi\u2019s entropy. Pattern Recognit 37:1149\u20131161. https:\/\/doi.org\/10.1016\/j.patcog.2003.10.008","journal-title":"Pattern Recognit"},{"key":"4842_CR30","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/0734-189X(88)90022-9","volume":"41","author":"P Sahoo","year":"1988","unstructured":"Sahoo P, Soltani S, Wong AK (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41:233\u2013260. https:\/\/doi.org\/10.1016\/0734-189X(88)90022-9","journal-title":"Comput Vis Graph Image Process"},{"key":"4842_CR31","unstructured":"Salhi A, Fraga ES (2011) Nature-inspired optimisation approaches and the new plant propagation algorithm, pp 1\u20138"},{"key":"4842_CR32","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/J.SCIENT.2011.04.003","volume":"18","author":"S Sarafrazi","year":"2011","unstructured":"Sarafrazi S, Nezamabadi-pour H, Saryazdi S (2011) Disruption: a new operator in gravitational search algorithm. Sci Iran 18:539\u2013548. https:\/\/doi.org\/10.1016\/J.SCIENT.2011.04.003","journal-title":"Sci Iran"},{"key":"4842_CR33","doi-asserted-by":"crossref","unstructured":"Sarkar S, Das S, Chaudhuri SS (2012) Multilevel image thresholding based on Tsallis entropy and differential evolution. In: Swarm, evolutionary, and memetic computing, SEMCCO 2012","DOI":"10.1007\/978-3-642-35380-2_3"},{"key":"4842_CR34","doi-asserted-by":"publisher","first-page":"15549","DOI":"10.1016\/j.eswa.2011.06.004","volume":"38","author":"PD Sathya","year":"2011","unstructured":"Sathya PD, Kayalvizhi R (2011) Optimal multilevel thresholding using bacterial foraging algorithm. Expert Syst Appl 38:15549\u201315564. https:\/\/doi.org\/10.1016\/j.eswa.2011.06.004","journal-title":"Expert Syst Appl"},{"key":"4842_CR35","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341\u2013359","journal-title":"J Glob Optim"},{"key":"4842_CR36","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1023\/A:1016540724870","volume":"8","author":"E-G Talbi","year":"2002","unstructured":"Talbi E-G (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8:541\u2013564. https:\/\/doi.org\/10.1023\/A:1016540724870","journal-title":"J Heuristics"},{"key":"4842_CR37","doi-asserted-by":"publisher","DOI":"10.1002\/9780470496916","volume-title":"Metaheuristics: from design to implementation","author":"E-G Talbi","year":"2009","unstructured":"Talbi E-G (2009) Metaheuristics: from design to implementation, 1st edn. Wiley, New York","edition":"1"},{"key":"4842_CR38","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1016\/S0167-8655(03)00166-1","volume":"24","author":"WB Tao","year":"2003","unstructured":"Tao WB, Tian JW, Liu J (2003) Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm. Pattern Recognit Lett 24:3069\u20133078. https:\/\/doi.org\/10.1016\/S0167-8655(03)00166-1","journal-title":"Pattern Recognit Lett"},{"key":"4842_CR39","doi-asserted-by":"crossref","unstructured":"Tian W, Geng Y, Liu J, Ai L (2009) Maximum fuzzy entropy and immune clone selection algorithm for image segmentation. In: 2009 Asia-Pacific conference on information processing. IEEE, Shenzhen, pp 38\u201341","DOI":"10.1109\/APCIP.2009.18"},{"key":"4842_CR40","volume-title":"Fuzzy image processing (in German)","author":"HR Tizhoosh","year":"1998","unstructured":"Tizhoosh HR (1998) Fuzzy image processing (in German). Springer, Berlin"},{"key":"4842_CR41","doi-asserted-by":"publisher","first-page":"2363","DOI":"10.1016\/j.patcog.2005.02.014","volume":"38","author":"HR Tizhoosh","year":"2005","unstructured":"Tizhoosh HR (2005) Image thresholding using type II fuzzy sets. Pattern Recognit 38:2363\u20132372. https:\/\/doi.org\/10.1016\/j.patcog.2005.02.014","journal-title":"Pattern Recognit"},{"key":"4842_CR42","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/978-3-540-73723-0_31","volume":"220","author":"HR Tizhoosh","year":"2008","unstructured":"Tizhoosh HR (2008) Type II fuzzy image segmentation. Stud Fuzziness Soft Comput 220:607\u2013619. https:\/\/doi.org\/10.1007\/978-3-540-73723-0_31","journal-title":"Stud Fuzziness Soft Comput"},{"key":"4842_CR43","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"4842_CR44","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/91.928743","volume":"9","author":"M Zhao","year":"2001","unstructured":"Zhao M, Fu AMN, Yan H (2001) A technique of three-level thresholding based on probability partition and fuzzy 3-partition. IEEE Trans Fuzzy Syst 9:469\u2013479. https:\/\/doi.org\/10.1109\/91.928743","journal-title":"IEEE Trans Fuzzy Syst"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04842-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-020-04842-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04842-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T00:19:09Z","timestamp":1615594749000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-020-04842-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,13]]},"references-count":44,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["4842"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-04842-7","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,13]]},"assertion":[{"value":"13 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"It is to specifically state that \u201cNo Competing interests are at stake and there is No Conflict of Interest\u201d with other people or organizations that could inappropriately influence or bias the content of the paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict 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":"Human and animal rights"}}]}}