{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:00Z","timestamp":1740108060616,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T00:00:00Z","timestamp":1562803200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T00:00:00Z","timestamp":1562803200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61571361","61671377"],"award-info":[{"award-number":["61571361","61671377"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science Plan Foundation of the Education Bureau of Shaanxi Province","award":["15JK1682"],"award-info":[{"award-number":["15JK1682"]}]},{"name":"Scientific Research Climbing Project of Xiamen University of Technology","award":["XPDKT18016"],"award-info":[{"award-number":["XPDKT18016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s00521-019-04333-3","type":"journal-article","created":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T13:49:41Z","timestamp":1562852981000},"page":"8431-8449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A novel relative homogeneity thresholding method with optimization strategy"],"prefix":"10.1007","volume":"32","author":[{"given":"Hong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Jui","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiulun","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,7,11]]},"reference":[{"issue":"3","key":"4333_CR1","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1109\/TSMC.2016.2608831","volume":"48","author":"HF Sima","year":"2018","unstructured":"Sima HF, Guo P, Zou YF, Wang ZH, Xu ML (2018) Bottom-up merging segmentation for color images with complex areas. IEEE Trans Syst Man Cybern Syst 48(3):354\u2013365","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"2","key":"4333_CR2","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2018","unstructured":"Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai WJ, Caballero J, Cook SA, Marvao A, Dawes T, O\u2019Regan DP, Kainz B, Glocker B, Rueckert D (2018) Anatomically Constrained Neural Networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans Med Image\u00a037(2):384\u2013395","journal-title":"IEEE Trans Med Image"},{"issue":"1","key":"4333_CR3","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1109\/TIP.2018.2867941","volume":"28","author":"T Wang","year":"2019","unstructured":"Wang T, Yang J, Ji ZX, Sun QS (2019) Probabilistic diffusion for interactive image segmentation. IEEE Trans Image Process 28(1):330\u2013342","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"4333_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2018.01.091","volume":"292","author":"F Garcia-Lamont","year":"2018","unstructured":"Garcia-Lamont F, Cervantes J, L\u00f3pez A, Rodriguez L (2018) Segmentation of images by color features: a survey. Neurocomputing 292(5):1\u201327","journal-title":"Neurocomputing"},{"issue":"1","key":"4333_CR5","first-page":"164","volume":"40","author":"A Eltanboly","year":"2019","unstructured":"Eltanboly A, Ghazal M, Hajjdiab H, Shalaby A, Switala A, Mahmoud A, Sahoo P, El-Azab M, El-Baz A (2019) Level sets-based image segmentation approach using statistical shape priors. Appl Math Comput 40(1):164\u2013179","journal-title":"Appl Math Comput"},{"key":"4333_CR6","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2017.02.008","volume":"38","author":"CF Shi","year":"2017","unstructured":"Shi CF, Cheng YZ, Wang JK, Wang YD, Mori K, Tamura S (2017) Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Med Image Anal 38:30\u201349","journal-title":"Med Image Anal"},{"key":"4333_CR7","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.media.2015.04.015","volume":"23","author":"T Tong","year":"2015","unstructured":"Tong T, Wolz R, Wang ZH, Gao QQ, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal 23:92\u2013104","journal-title":"Med Image Anal"},{"issue":"2","key":"4333_CR8","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/0734-189X(88)90022-9","volume":"41","author":"PK Sahoo","year":"1988","unstructured":"Sahoo PK, Soltani S, Wong AKC (1988) A survey of thresholding techniques. Comput Vis Graphics Image Process 41(2):233\u2013260","journal-title":"Comput Vis Graphics Image Process"},{"issue":"1","key":"4333_CR9","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(1):62\u201366","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"4333_CR10","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.measurement.2017.09.052","volume":"114","author":"TY Goh","year":"2018","unstructured":"Goh TY, Basah SN, Yazid H, Safar MJA, Saad FSA (2018) Performance analysis of image thresholding: Otsu technique. Measurement 114:298\u2013307","journal-title":"Measurement"},{"key":"4333_CR11","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1016\/j.patrec.2012.01.002","volume":"33","author":"JH Xue","year":"2012","unstructured":"Xue JH, Zhang YJ (2012) Ridler and Calvard\u2019s, Kittler and Illingworth\u2019s and Otsu\u2019s methods for image thresholding. Pattern Recognit Lett 33:793\u2013797","journal-title":"Pattern Recognit Lett"},{"key":"4333_CR12","doi-asserted-by":"publisher","first-page":"2392","DOI":"10.1109\/TIP.2011.2114358","volume":"20","author":"JH Xue","year":"2011","unstructured":"Xue JH, Titterington DM (2011) t-Test, F-tests and Otsu\u2019s methods for image thresholding. IEEE Trans Image Process 20:2392\u20132396","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"4333_CR13","first-page":"387","volume":"2","author":"HJ Vala","year":"2013","unstructured":"Vala HJ, Baxi A (2013) A review on Otsu image segmentation algorithm. Int J Adv Res Comput Eng Technol 2(2):387\u2013389","journal-title":"Int J Adv Res Comput Eng Technol"},{"key":"4333_CR14","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.dsp.2014.02.008","volume":"28","author":"Y Zou","year":"2014","unstructured":"Zou Y, Dong F, Lei B, Sun S, Jiang T, Chen P (2014) Maximum similarity thresholding. Digit Signal Proc 28:120\u2013135","journal-title":"Digit Signal Proc"},{"issue":"6","key":"4333_CR15","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/j.patrec.2011.12.009","volume":"33","author":"JL Fan","year":"2012","unstructured":"Fan JL, Lei B (2012) A modified valley-emphasis method for automatic thresholding. Pattern Recognit Lett 33(6):703\u2013708","journal-title":"Pattern Recognit Lett"},{"issue":"15","key":"4333_CR16","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.apsusc.2015.05.033","volume":"349","author":"XC Yuan","year":"2015","unstructured":"Yuan XC, Wu LS, Peng QJ (2015) An improved Otsu method using the weighted object variance for defect detection. Appl Surf Sci 349(15):472\u2013484","journal-title":"Appl Surf Sci"},{"key":"4333_CR17","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.compeleceng.2015.03.025","volume":"46","author":"M Suheir","year":"2015","unstructured":"Suheir M, Harb E, Isa NAM, Salamah SA (2015) Improved image magnification algorithm based on Otsu thresholding. Comput Electr Eng 46:338\u2013355","journal-title":"Comput Electr Eng"},{"issue":"6","key":"4333_CR18","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1016\/j.patcog.2011.12.013","volume":"45","author":"R Farrahi Moghaddam","year":"2012","unstructured":"Farrahi Moghaddam R, Cheriet M (2012) AdOtsu: an adaptive and parameterless generalization of Otsu\u2019s method for document image binarization. Pattern Recognit 45(6):2419\u20132431","journal-title":"Pattern Recognit"},{"issue":"3","key":"4333_CR19","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1109\/TIP.2013.2297014","volume":"23","author":"YK Lai","year":"2014","unstructured":"Lai YK, Rosin PL (2014) Efficient circular thresholding. IEEE Trans Image Process 23(3):992\u20131001","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"4333_CR20","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/TIP.2014.2298981","volume":"23","author":"H Cai","year":"2014","unstructured":"Cai H, Yang Z, Cao X, Xia W, Xu X (2014) A new iterative triclass thresholding technique in image segmentation. IEEE Trans Image Process 23(3):1038\u20131046","journal-title":"IEEE Trans Image Process"},{"key":"4333_CR21","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.dsp.2016.08.003","volume":"60","author":"YC Feng","year":"2017","unstructured":"Feng YC, Zhao HY, Li XF, Zhang XL, Li HP (2017) A multi-scale 3D Otsu thresholding algorithm for medical image segmentation. Digit Signal Proc 60:186\u2013199","journal-title":"Digit Signal Proc"},{"key":"4333_CR22","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.patcog.2019.01.025","volume":"91","author":"S He","year":"2019","unstructured":"He S, Schomaker L (2019) DeepOtsu: document enhancement and binarization using iterative deep learning. Pattern Recognit 91:379\u2013390","journal-title":"Pattern Recognit"},{"key":"4333_CR23","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.measurement.2013.09.031","volume":"47","author":"S Manikandan","year":"2014","unstructured":"Manikandan S, Ramar K, Willjuice Iruthayarajan M, Srinivasagan KG (2014) Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm. Measurement 47:558\u2013568","journal-title":"Measurement"},{"issue":"3","key":"4333_CR24","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 (2015) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur\u2019s, Otsu and Tsallis functions. Expert Syst Appl 42(3):1573\u20131601","journal-title":"Expert Syst Appl"},{"key":"4333_CR25","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.neucom.2018.05.116","volume":"312","author":"F Zhao","year":"2018","unstructured":"Zhao\u00a0F, Liu\u00a0HQ,\u00a0Fan\u00a0JL,\u00a0Chen\u00a0CW,\u00a0Lan\u00a0R,\u00a0Li\u00a0N (2018)\u00a0Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation. Neurocomputing\u00a0312:296\u2013309","journal-title":"Neurocomputing"},{"issue":"16\u201318","key":"4333_CR26","doi-asserted-by":"publisher","first-page":"2411","DOI":"10.1016\/j.neucom.2006.02.014","volume":"69","author":"SC Chen","year":"2006","unstructured":"Chen SC, Li DH (2006) Image binarization focusing on objects. Neurocomputing 69(16\u201318):2411\u20132415","journal-title":"Neurocomputing"},{"issue":"2","key":"4333_CR27","first-page":"285","volume":"9","author":"H Zhang","year":"2018","unstructured":"Zhang H, Hu WY (2018) A modified thresholding method based on relative homogeneity. J Inf Hiding Multimed Signal Process 9(2):285\u2013292","journal-title":"J Inf Hiding Multimed Signal Process"},{"issue":"1","key":"4333_CR28","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1117\/1.1631315","volume":"13","author":"M Sezgin","year":"2004","unstructured":"Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146\u2013168","journal-title":"J Electron Imaging"},{"doi-asserted-by":"crossref","unstructured":"Sezgin M, Sankur B (2003) Image multithresholding based on sample moment function. In: Proceeding of the 2003 IEEE international conference on image processing, vol 9. Barcelona, Spain, pp 415\u2013418","key":"4333_CR29","DOI":"10.1109\/ICIP.2003.1246705"},{"unstructured":"Sezgin M (2017) blt_image_references. \n                  http:\/\/mehmetsezgin.net\n                  \n                . Accessed 10 Nov 2017","key":"4333_CR30"},{"key":"4333_CR31","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.1016\/j.asoc.2012.03.072","volume":"13","author":"B Akay","year":"2013","unstructured":"Akay B (2013) A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl Soft Comput 13:3066\u20133091","journal-title":"Appl Soft Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04333-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04333-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04333-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:21:09Z","timestamp":1594340469000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04333-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,11]]},"references-count":31,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["4333"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04333-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2019,7,11]]},"assertion":[{"value":"22 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}