{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:36:58Z","timestamp":1778805418053,"version":"3.51.4"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T00:00:00Z","timestamp":1613865600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T00:00:00Z","timestamp":1613865600000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s00521-021-05771-8","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T15:03:05Z","timestamp":1613919785000},"page":"10057-10091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An accurate Cluster chaotic optimization approach for digital medical image segmentation"],"prefix":"10.1007","volume":"33","author":[{"given":"Omar","family":"Avalos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernesto","family":"Ayala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando","family":"Wario","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"P\u00e9rez-Cisneros","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"5771_CR1","unstructured":"Purri M, Xue J, Dana K, et al (2019) Material segmentation of multi-view satellite imagery. arXiv"},{"key":"5771_CR2","doi-asserted-by":"publisher","first-page":"134448","DOI":"10.1109\/ACCESS.2019.2942064","volume":"7","author":"H Jia","year":"2019","unstructured":"Jia H, Sun K, Song W et al (2019) Multi-strategy emperor penguin optimizer for RGB histogram-based color satellite image segmentation using masi entropy. IEEE Access 7:134448\u2013134474. https:\/\/doi.org\/10.1109\/ACCESS.2019.2942064","journal-title":"IEEE Access"},{"key":"5771_CR3","doi-asserted-by":"publisher","first-page":"17197","DOI":"10.1007\/s11042-018-7034-x","volume":"78","author":"S Shubham","year":"2019","unstructured":"Shubham S, Bhandari AK (2019) A generalized Masi entropy based efficient multi-level thresholding method for color image segmentation. Multimed Tools Appl 78:17197\u201317238. https:\/\/doi.org\/10.1007\/s11042-018-7034-x","journal-title":"Multimed Tools Appl"},{"key":"5771_CR4","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.3390\/rs10071039","volume":"10","author":"M Wan","year":"2018","unstructured":"Wan M, Gu G, Sun J et al (2018) A level set method for infrared image segmentation using global and local information. Remote Sens 10:1039. https:\/\/doi.org\/10.3390\/rs10071039","journal-title":"Remote Sens"},{"key":"5771_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jneumeth.2018.02.017","volume":"301","author":"K Mutlu","year":"2018","unstructured":"Mutlu K, Rabell JE, Martin del Olmo P, Haesler S (2018) IR thermography-based monitoring of respiration phase without image segmentation. J Neurosci Methods 301:1\u20138. https:\/\/doi.org\/10.1016\/j.jneumeth.2018.02.017","journal-title":"J Neurosci Methods"},{"key":"5771_CR6","doi-asserted-by":"crossref","unstructured":"Lu TT, Huyen A, Payumo K et al. (2018) Deep neural network for precision multi-band infrared image segmentation. In: Alam MS (ed) Pattern recognition and tracking XXIX. SPIE, p 3","DOI":"10.1117\/12.2305134"},{"key":"5771_CR7","doi-asserted-by":"crossref","unstructured":"Zhao A, Balakrishnan G, Durand F et al. (2019) Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pp 8535\u20138545","DOI":"10.1109\/CVPR.2019.00874"},{"key":"5771_CR8","doi-asserted-by":"crossref","unstructured":"Chen X, Williams BM, Vallabhaneni SR et al. (2019) Learning active contour models for medical image segmentation. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pp 11624\u201311632","DOI":"10.1109\/CVPR.2019.01190"},{"key":"5771_CR9","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imag 32:582\u2013596. https:\/\/doi.org\/10.1007\/s10278-019-00227-x","journal-title":"J Digit Imag"},{"key":"5771_CR10","doi-asserted-by":"publisher","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","volume":"7","author":"Y Weng","year":"2019","unstructured":"Weng Y, Zhou T, Li Y, Qiu X (2019) NAS-Unet: nural architecture search for medical image segmentation. IEEE Access 7:44247\u201344257. https:\/\/doi.org\/10.1109\/ACCESS.2019.2908991","journal-title":"IEEE Access"},{"key":"5771_CR11","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/MIS.2016.24","volume":"31","author":"KC Santosh","year":"2016","unstructured":"Santosh KC, Wendling L, Antani S, Thoma GR (2016) Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell Syst 31:66\u201375. https:\/\/doi.org\/10.1109\/MIS.2016.24","journal-title":"IEEE Intell Syst"},{"key":"5771_CR12","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s13042-018-0813-x","volume":"10","author":"SP Vaidya","year":"2019","unstructured":"Vaidya SP, Mouli PVSSRC, Santosh KC (2019) Imperceptible watermark for a game-theoretic watermarking system. Int J Mach Learn Cybern 10:1323\u20131339. https:\/\/doi.org\/10.1007\/s13042-018-0813-x","journal-title":"Int J Mach Learn Cybern"},{"key":"5771_CR13","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2017.02.040","volume":"240","author":"L He","year":"2017","unstructured":"He L, Huang S (2017) Modified firefly algorithm based multi-level thresholding for color image segmentation. Neurocomputing 240:152\u2013174. https:\/\/doi.org\/10.1016\/j.neucom.2017.02.040","journal-title":"Neurocomputing"},{"key":"5771_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.sigpro.2012.07.010","volume":"93","author":"A Dirami","year":"2013","unstructured":"Dirami A, Hammouche K, Diaf M, Siarry P (2013) Fast multi-level thresholding for image segmentation through a multiphase level set method. Signal Process 93:139\u2013153. https:\/\/doi.org\/10.1016\/j.sigpro.2012.07.010","journal-title":"Signal Process"},{"key":"5771_CR15","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":"5771_CR16","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/0734-189x(85)90133-1","volume":"29","author":"WH Tsai","year":"1985","unstructured":"Tsai WH (1985) Moment-preserving thresholding: a new approach. Comput Vision Graph Image Process 29:377\u2013393. https:\/\/doi.org\/10.1016\/0734-189x(85)90133-1","journal-title":"Comput Vision Graph Image Process"},{"key":"5771_CR17","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 Vision Graph Image Process 29:273\u2013285. https:\/\/doi.org\/10.1016\/0734-189X(85)90125-2","journal-title":"Comput Vision Graph Image Process"},{"key":"5771_CR18","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":"5771_CR19","doi-asserted-by":"crossref","unstructured":"Laurenceau J, Meaux M (2008) Comparison of gradient and response surface based optimization frameworks using adjoint method. American Institute of Aeronautics and Astronautics (AIAA)","DOI":"10.2514\/6.2008-1889"},{"key":"5771_CR20","doi-asserted-by":"publisher","first-page":"3022","DOI":"10.2514\/1.21744","volume":"44","author":"RP Dwight","year":"2006","unstructured":"Dwight RP, Brezillon J (2006) Effect of approximations of the discrete adjoint on gradient-based optimization. AIAA J 44:3022\u20133031. https:\/\/doi.org\/10.2514\/1.21744","journal-title":"AIAA J"},{"key":"5771_CR21","first-page":"989","volume-title":"A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Building and environment","author":"M Wetter","year":"2004","unstructured":"Wetter M, Wright J (2004) A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Building and environment. Elsevier BV, Amsterdam, pp 989\u2013999"},{"key":"5771_CR22","unstructured":"Wiley: Evolutionary optimization algorithms: Dan Simon. https:\/\/books.google.com.mx\/books?hl=es&lr=&id=gwUwIEPqk30C&oi=fnd&pg=PP1&dq=evolutionary+optimization+algorithms&ots=GLs1FlN9i2&sig=gnFLR6xSr7j1gLZjIdc_Aw83xYY#v=onepage&q&f=false. Accessed 23 Jul 2020"},{"key":"5771_CR23","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms: computer programs that \u201cevolve\u201d in ways that resemble natural selection can solve complex problems even their creators do not fully understand. Sci Am 267:66\u201372","journal-title":"Sci Am"},{"key":"5771_CR24","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/0303-2647(94)90062-0","volume":"33","author":"PJ Angeline","year":"1994","unstructured":"Angeline PJ (1994) Genetic programming: on the programming of computers by means of natural selection. Biosystems 33:69\u201373. https:\/\/doi.org\/10.1016\/0303-2647(94)90062-0","journal-title":"Biosystems"},{"key":"5771_CR25","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural networks, 1995 proceedings, IEEE international conference 4:1942\u20131948 vol 4. https:\/\/doi.org\/https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"5771_CR26","doi-asserted-by":"crossref","unstructured":"Karaboga D, Basturk B (2007) Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin P, Castillo O, Aguilar LT, et al (eds) Foundations of fuzzy logic and soft computing: 12th international fuzzy systems association world congress, IFSA 2007, Cancun, Mexico, June 18\u201321, 2007. Proceedings. Springer, Berlin, pp 789\u2013798","DOI":"10.1007\/978-3-540-72950-1_77"},{"key":"5771_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","volume":"169","author":"A Askarzadeh","year":"2016","unstructured":"Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1\u201312. https:\/\/doi.org\/10.1016\/j.compstruc.2016.03.001","journal-title":"Comput Struct"},{"key":"5771_CR28","doi-asserted-by":"publisher","unstructured":"Yang XS, Deb S (2009) Cuckoo search via L\u00e9vy flights. In: 2009 world congress on nature and biologically inspired computing NABIC 2009-Proc 210\u2013214. https:\/\/doi.org\/10.1109\/NABIC.2009.5393690","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"5771_CR29","unstructured":"Dorigo M, on GDC-P of the 1999 congress, 1999 undefined Ant colony optimization: a new meta-heuristic. ieeexplore.ieee.org"},{"key":"5771_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystems.2018.09.007","volume-title":"A novel bio-inspired optimization model based on yellow saddle goatfish behavior","author":"D Zaldivar","year":"2018","unstructured":"Zaldivar D, Morales B, Rodr\u00edguez A et al (2018) A novel bio-inspired optimization model based on yellow saddle goatfish behavior. Elsevier, Amsterdam"},{"key":"5771_CR31","doi-asserted-by":"publisher","first-page":"6374","DOI":"10.1016\/j.eswa.2013.05.041","volume":"40","author":"E Cuevas","year":"2013","unstructured":"Cuevas E, Cienfuegos M, Zald\u00edvar D, P\u00e9rez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40:6374\u20136384. https:\/\/doi.org\/10.1016\/j.eswa.2013.05.041","journal-title":"Expert Syst Appl"},{"key":"5771_CR32","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228\u2013249. https:\/\/doi.org\/10.1016\/j.knosys.2015.07.006","journal-title":"Knowl Based Syst"},{"key":"5771_CR33","unstructured":"Rai GNH, Nair TRG (2010) Gradient based seeded region grow method for CT angiographic image segmentation. InterJRI Comput Sci Netw 1(1)"},{"key":"5771_CR34","unstructured":"Akram MU, Nasir S, Tariq A et al. (2008) Improved fingerprint image segmentation using new modified gradient based technique. In: Canadian conference on electrical and computer engineering. pp 1967\u20131971"},{"key":"5771_CR35","doi-asserted-by":"publisher","first-page":"1618","DOI":"10.1109\/TIP.2003.819311","volume":"12","author":"PR Hill","year":"2003","unstructured":"Hill PR, Nishan Canagarajah C, Bull DR (2003) Image segmentation using a texture gradient based watershed transform. IEEE Trans Image Process 12:1618\u20131633. https:\/\/doi.org\/10.1109\/TIP.2003.819311","journal-title":"IEEE Trans Image Process"},{"key":"5771_CR36","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.eswa.2016.08.046","volume":"65","author":"U Mlakar","year":"2016","unstructured":"Mlakar U, Poto\u010dnik B, Brest J (2016) A hybrid differential evolution for optimal multi-level image thresholding. Expert Syst Appl 65:221\u2013232. https:\/\/doi.org\/10.1016\/j.eswa.2016.08.046","journal-title":"Expert Syst Appl"},{"key":"5771_CR37","first-page":"379","volume-title":"Robust color image multi-thresholding using between-class variance and cuckoo search algorithm. Advances in intelligent systems and computing","author":"V Rajinikanth","year":"2016","unstructured":"Rajinikanth V, Sri Madhava Raja N, Satapathy SC (2016) Robust color image multi-thresholding using between-class variance and cuckoo search algorithm. Advances in intelligent systems and computing. Springer, Berlin, pp 379\u2013386"},{"key":"5771_CR38","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.compeleceng.2017.08.008","volume":"70","author":"S Pare","year":"2018","unstructured":"Pare S, Bhandari AK, Kumar A, Singh GK (2018) A new technique for multi-level color image thresholding based on modified fuzzy entropy and L\u00e9vy flight firefly algorithm. Comput Electr Eng 70:476\u2013495. https:\/\/doi.org\/10.1016\/j.compeleceng.2017.08.008","journal-title":"Comput Electr Eng"},{"key":"5771_CR39","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.asoc.2016.04.024","volume":"56","author":"S Dey","year":"2017","unstructured":"Dey S, Bhattacharyya S, Maulik U (2017) Efficient quantum inspired meta-heuristics for multi-level true colour image thresholding. Appl Soft Comput J 56:472\u2013513. https:\/\/doi.org\/10.1016\/j.asoc.2016.04.024","journal-title":"Appl Soft Comput J"},{"key":"5771_CR40","doi-asserted-by":"publisher","first-page":"30508","DOI":"10.1109\/ACCESS.2018.2837062","volume":"6","author":"L Shen","year":"2018","unstructured":"Shen L, Fan C, Huang X (2018) Multi-level image thresholding using modified flower pollination algorithm. IEEE Access 6:30508\u201330519. https:\/\/doi.org\/10.1109\/ACCESS.2018.2837062","journal-title":"IEEE Access"},{"key":"5771_CR41","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2018.2859429","author":"S Agrawal","year":"2018","unstructured":"Agrawal S, Panda R, Abraham A (2018) A novel diagonal class entropy-based multi-level image thresholding using coral reef optimization. IEEE Trans Syst Man, Cybern Syst. https:\/\/doi.org\/10.1109\/TSMC.2018.2859429","journal-title":"IEEE Trans Syst Man, Cybern Syst"},{"key":"5771_CR42","first-page":"33","volume":"4","author":"F Shahabi","year":"2019","unstructured":"Shahabi F, Pourahangarian F, Beheshti H (2019) A multi-level image thresholding approach based on crow search algorithm and Otsu method. Decis Oper Res 4:33\u201341","journal-title":"Decis Oper Res"},{"key":"5771_CR43","doi-asserted-by":"publisher","first-page":"181405","DOI":"10.1109\/ACCESS.2019.2959325","volume":"7","author":"HSN Alwerfali","year":"2019","unstructured":"Alwerfali HSN, Abd Elaziz M, Al-Qaness MAA et al (2019) A Multi-level image thresholding based on hybrid salp swarm algorithm and fuzzy entropy. IEEE Access 7:181405\u2013181422. https:\/\/doi.org\/10.1109\/ACCESS.2019.2959325","journal-title":"IEEE Access"},{"key":"5771_CR44","unstructured":"Huang X, Shen L, Fan C et al. (2020) Multilevel image thresholding using a fully informed cuckoo search algorithm. arXiv"},{"key":"5771_CR45","doi-asserted-by":"publisher","first-page":"2050015","DOI":"10.1142\/S1469026820500157","volume":"19","author":"F Shahabi","year":"2020","unstructured":"Shahabi F, Poorahangaryan F, Edalatpanah SA, Beheshti H (2020) A multi-level image thresholding approach based on crow search algorithm and Otsu method. Int J Comput Intell Appl 19:2050015. https:\/\/doi.org\/10.1142\/S1469026820500157","journal-title":"Int J Comput Intell Appl"},{"key":"5771_CR46","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67\u201382. https:\/\/doi.org\/10.1109\/4235.585893","journal-title":"IEEE Trans Evol Comput"},{"key":"5771_CR47","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s13042-019-00979-6","volume":"11","author":"J G\u00e1lvez","year":"2020","unstructured":"G\u00e1lvez J, Cuevas E, Becerra H, Avalos O (2020) A hybrid optimization approach based on clustering and chaotic sequences. Int J Mach Learn Cybern 11:359\u2013401. https:\/\/doi.org\/10.1007\/s13042-019-00979-6","journal-title":"Int J Mach Learn Cybern"},{"key":"5771_CR48","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.eswa.2017.04.023","volume":"83","author":"AMA El","year":"2017","unstructured":"El AMA, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multi-level thresholding image segmentation. Expert Syst Appl 83:242\u2013256. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.023","journal-title":"Expert Syst Appl"},{"key":"5771_CR49","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.eswa.2019.01.075","volume":"125","author":"MA Elaziz","year":"2019","unstructured":"Elaziz MA, Lu S (2019) Many-objectives multi-level thresholding image segmentation using knee evolutionary algorithm. Expert Syst Appl 125:305\u2013316. https:\/\/doi.org\/10.1016\/j.eswa.2019.01.075","journal-title":"Expert Syst Appl"},{"key":"5771_CR50","doi-asserted-by":"publisher","first-page":"113428","DOI":"10.1016\/j.eswa.2020.113428","volume":"155","author":"E Rodr\u00edguez-Esparza","year":"2020","unstructured":"Rodr\u00edguez-Esparza E, Zanella-Calzada LA, Oliva D et al (2020) An efficient Harris hawks-inspired image segmentation method. Expert Syst Appl 155:113428. https:\/\/doi.org\/10.1016\/j.eswa.2020.113428","journal-title":"Expert Syst Appl"},{"key":"5771_CR51","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.eswa.2017.02.042","volume":"79","author":"D Oliva","year":"2017","unstructured":"Oliva D, Hinojosa S, Cuevas E et al (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79:164\u2013180. https:\/\/doi.org\/10.1016\/j.eswa.2017.02.042","journal-title":"Expert Syst Appl"},{"key":"5771_CR52","doi-asserted-by":"crossref","unstructured":"Labati RD, Piuri V, Scotti F (2011) All-IDB: the acute lymphoblastic leukemia image database for image processing. In: 2011 18th IEEE international conference on image processing. IEEE, pp 2045\u20132048","DOI":"10.1109\/ICIP.2011.6115881"},{"key":"5771_CR53","doi-asserted-by":"publisher","first-page":"9514707","DOI":"10.1155\/2016\/9514707","volume":"2016","author":"Y Li","year":"2016","unstructured":"Li Y, Zhu R, Mi L et al (2016) Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method. Comput Math Methods Med 2016:9514707. https:\/\/doi.org\/10.1155\/2016\/9514707","journal-title":"Comput Math Methods Med"},{"key":"5771_CR54","unstructured":"USF Digital Mammography Home Page. http:\/\/www.eng.usf.edu\/cvprg\/Mammography\/Database.html. Accessed 6 Jul 2020"},{"key":"5771_CR55","doi-asserted-by":"crossref","unstructured":"Hor\u00e9 A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In: Proceedings-international conference on pattern recognition. pp 2366\u20132369","DOI":"10.1109\/ICPR.2010.579"},{"key":"5771_CR56","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":"5771_CR57","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20:2378\u20132386. https:\/\/doi.org\/10.1109\/TIP.2011.2109730","journal-title":"IEEE Trans Image Process"},{"key":"5771_CR58","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.amc.2006.06.057","volume":"184","author":"PY Yin","year":"2007","unstructured":"Yin PY (2007) Multi-level minimum cross entropy threshold selection based on particle swarm optimization. Appl Math Comput 184:503\u2013513. https:\/\/doi.org\/10.1016\/j.amc.2006.06.057","journal-title":"Appl Math Comput"},{"key":"5771_CR59","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Glob Optim"},{"key":"5771_CR60","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1177\/003754970107600201","volume":"762","author":"ZW Geem","year":"2001","unstructured":"Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 762:60\u201368","journal-title":"Simulation"},{"key":"5771_CR61","volume-title":"Digital image processing using MATLAB","author":"RC Gonzalez","year":"2004","unstructured":"Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Upper Saddle River, NJ, Pearson\/Prentice Hall"},{"key":"5771_CR62","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/s00357-014-9161-z","volume":"31","author":"F Murtagh","year":"2014","unstructured":"Murtagh F, Legendre P (2014) Ward\u2019s hierarchical agglomerative clustering method: which algorithms implement ward\u2019s criterion? J Classif 31:274\u2013295. https:\/\/doi.org\/10.1007\/s00357-014-9161-z","journal-title":"J Classif"},{"key":"5771_CR63","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JH Ward","year":"1963","unstructured":"Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236\u2013244. https:\/\/doi.org\/10.1080\/01621459.1963.10500845","journal-title":"J Am Stat Assoc"},{"key":"5771_CR64","doi-asserted-by":"publisher","first-page":"26304","DOI":"10.1109\/ACCESS.2020.2971249","volume":"8","author":"AA Ewees","year":"2020","unstructured":"Ewees AA, Abd Elaziz M, Al-Qaness MAA et al (2020) Improved artificial bee colony using sine-cosine algorithm for multi-level thresholding image segmentation. IEEE Access 8:26304\u201326315. https:\/\/doi.org\/10.1109\/ACCESS.2020.2971249","journal-title":"IEEE Access"},{"key":"5771_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.04.002","author":"SJ Mousavirad","year":"2019","unstructured":"Mousavirad SJ, Ebrahimpour-Komleh H (2019) Human mental search-based multi-level thresholding for image segmentation. Appl Soft Comput J. https:\/\/doi.org\/10.1016\/j.asoc.2019.04.002","journal-title":"Appl Soft Comput J"},{"key":"5771_CR66","doi-asserted-by":"publisher","first-page":"12407","DOI":"10.1016\/j.eswa.2012.04.078","volume":"39","author":"P Ghamisi","year":"2012","unstructured":"Ghamisi P, Couceiro MS, Benediktsson JA, Ferreira NMF (2012) An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst Appl 39:12407\u201312417. https:\/\/doi.org\/10.1016\/j.eswa.2012.04.078","journal-title":"Expert Syst Appl"},{"key":"5771_CR67","doi-asserted-by":"publisher","unstructured":"Flynn JR, Ward S, Abich J, Poole D (2013) Image quality assessment using the SSIM and the just noticeable difference paradigm. In: Harris D (ed) Engineering psychology and cognitive ergonomics. Understanding human cognition. EPCE 2013. Lecture Notes in Computer Science, vol 8019. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-39360-0_3","DOI":"10.1007\/978-3-642-39360-0_3"},{"key":"5771_CR68","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s11760-019-01585-3","volume":"14","author":"X Yue","year":"2020","unstructured":"Yue X, Zhang H (2020) A multi-level image thresholding approach using Otsu based on the improved invasive weed optimization algorithm. Signal Image Video Process 14:575\u2013582. https:\/\/doi.org\/10.1007\/s11760-019-01585-3","journal-title":"Signal Image Video Process"},{"key":"5771_CR69","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.ins.2019.07.088","volume":"506","author":"MF Di","year":"2020","unstructured":"Di MF, Sessa S (2020) PSO image thresholding on images compressed via fuzzy transforms. Inf Sci (NY) 506:308\u2013324. https:\/\/doi.org\/10.1016\/j.ins.2019.07.088","journal-title":"Inf Sci (NY)"},{"key":"5771_CR70","doi-asserted-by":"crossref","unstructured":"Monisha R, Mrinalini R, Nithila Britto M et al. (2019) Social group optimization and Shannon\u2019s function-based RGB image multi-level thresholding. In: Smart Innovation, Systems and Technologies. Springer Science and Business Media Deutschland GmbH, pp 123\u2013132","DOI":"10.1007\/978-981-13-1927-3_13"},{"key":"5771_CR71","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04820-y","author":"M Abdel-Basset","year":"2020","unstructured":"Abdel-Basset M, Chang V, Mohamed R (2020) A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-04820-y","journal-title":"Neural Comput Appl"},{"key":"5771_CR72","doi-asserted-by":"crossref","unstructured":"Loizou CP, Pantziaris M, Seimenis I, Pattichis CS (2009) Brain MR image normalization in texture analysis of multiple sclerosis. In: final program and abstract Book-9th international conference on information technology and applications in biomedicine, ITAB 2009","DOI":"10.1109\/ITAB.2009.5394331"},{"key":"5771_CR73","first-page":"400","volume-title":"IFIP advances in information and communication technology","author":"CP Loizou","year":"2011","unstructured":"Loizou CP, Kyriacou EC, Seimenis I et al (2011) Brain white matter lesions classification in multiple sclerosis subjects for the prognosis of future disability. IFIP advances in information and communication technology. Springer, New York, pp 400\u2013409"},{"key":"5771_CR74","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TITB.2010.2091279","volume":"15","author":"CP Loizou","year":"2011","unstructured":"Loizou CP, Murray V, Pattichis MS et al (2011) Multiscale amplitude-modulation frequency-modulation (AMFM) texture analysis of multiple sclerosis in brain MRI images. IEEE Trans Inf Technol Biomed 15:119\u2013129. https:\/\/doi.org\/10.1109\/TITB.2010.2091279","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"5771_CR75","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.neurad.2014.05.006","volume":"42","author":"CP Loizou","year":"2015","unstructured":"Loizou CP, Petroudi S, Seimenis I et al (2015) Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. J Neuroradiol 42:99\u2013114. https:\/\/doi.org\/10.1016\/j.neurad.2014.05.006","journal-title":"J Neuroradiol"},{"key":"5771_CR76","doi-asserted-by":"crossref","unstructured":"Wang X, Peng Y, Lu L et al. (2017) ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. arXiv","DOI":"10.1109\/CVPR.2017.369"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05771-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05771-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05771-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T07:23:36Z","timestamp":1625729016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05771-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,21]]},"references-count":76,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["5771"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05771-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,21]]},"assertion":[{"value":"28 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2021","order":3,"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":"Authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}