{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:30:50Z","timestamp":1750221050397,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,10,13]],"date-time":"2018-10-13T00:00:00Z","timestamp":1539388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,10,13]]},"DOI":"10.1145\/3285996.3285997","type":"proceedings-article","created":{"date-parts":[[2019,1,7]],"date-time":"2019-01-07T14:44:51Z","timestamp":1546872291000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Local Gauss Multiplicative Components (LG-MC) Method for MR Image Segmentation"],"prefix":"10.1145","author":[{"given":"Jie","family":"Cheng","sequence":"first","affiliation":[{"name":"College of science, China University of Petroleum, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqing","family":"Yin","sequence":"additional","affiliation":[{"name":"College of science, China University of Petroleum, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of science, China University of Petroleum, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyu","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of science, China University of Petroleum, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su","family":"Wei","sequence":"additional","affiliation":[{"name":"College of science, China University of Petroleum, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,10,13]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1002\/jmri.22344"},{"doi-asserted-by":"crossref","unstructured":"M. A. Castro etal 2010. Template-based B 1 inhomogeneity correction in 3T MRI brain studies. IEEE transactions on medical imaging 29 11 (2010) 1927--1941.  M. A. Castro et al. 2010. Template-based B 1 inhomogeneity correction in 3T MRI brain studies. IEEE transactions on medical imaging 29 11 (2010) 1927--1941.","key":"e_1_3_2_1_2_1","DOI":"10.1109\/TMI.2010.2053552"},{"doi-asserted-by":"crossref","unstructured":"J. G. Sled A. P. Zijdenbos and A. C. Evans. 1998. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE transactions on medical imaging 17 1 (1998) 87--97.  J. G. Sled A. P. Zijdenbos and A. C. Evans. 1998. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE transactions on medical imaging 17 1 (1998) 87--97.","key":"e_1_3_2_1_3_1","DOI":"10.1109\/42.668698"},{"doi-asserted-by":"crossref","unstructured":"N. J. Tustison etal 2010. N4ITK: Improved N3 bias correction. IEEE transactions on medical imaging 29 6 (2010) 1310--1320.  N. J. Tustison et al. 2010. N4ITK: Improved N3 bias correction. IEEE transactions on medical imaging 29 6 (2010) 1310--1320.","key":"e_1_3_2_1_4_1","DOI":"10.1109\/TMI.2010.2046908"},{"doi-asserted-by":"crossref","unstructured":"B. Likar M. A. Viergever and F. Pernus. 2001. Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE transactions on medical imaging 20 12 (2010) 1398--1410.  B. Likar M. A. Viergever and F. Pernus. 2001. Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE transactions on medical imaging 20 12 (2010) 1398--1410.","key":"e_1_3_2_1_5_1","DOI":"10.1109\/42.974934"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.2214\/ajr.148.2.418"},{"doi-asserted-by":"crossref","unstructured":"B. H. Brinkmann A. Manduca and R. A. Robb. 1998. Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE transactions on medical imaging 17 2 (1998) 161--171.  B. H. Brinkmann A. Manduca and R. A. Robb. 1998. Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE transactions on medical imaging 17 2 (1998) 161--171.","key":"e_1_3_2_1_7_1","DOI":"10.1109\/42.700729"},{"doi-asserted-by":"crossref","unstructured":"B. J. Bedell P. A. Narayana and J. S. Wolinsky. 1997. A dual approach for minimizing false lesion classifications on magnetic resonance images. Magnetic resonance in medicine 37 1 (1997) 94--102.  B. J. Bedell P. A. Narayana and J. S. Wolinsky. 1997. A dual approach for minimizing false lesion classifications on magnetic resonance images. Magnetic resonance in medicine 37 1 (1997) 94--102.","key":"e_1_3_2_1_8_1","DOI":"10.1002\/mrm.1910370114"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1002\/jmri.22344"},{"doi-asserted-by":"crossref","unstructured":"A. Banerjee and P. Maji. 2013. Rough sets for bias field correction in MR images using contraharmonic mean and quantitative index. IEEE transactions on medical imaging 32 11 (2013) 2140--2151.  A. Banerjee and P. Maji. 2013. Rough sets for bias field correction in MR images using contraharmonic mean and quantitative index. IEEE transactions on medical imaging 32 11 (2013) 2140--2151.","key":"e_1_3_2_1_10_1","DOI":"10.1109\/TMI.2013.2274804"},{"doi-asserted-by":"crossref","unstructured":"D. L. Pham and J. L. Prince. 1999. Adaptive fuzzy segmentation of magnetic resonance images. IEEE transactions on medical imaging 18 9 (1999) 737--752.  D. L. Pham and J. L. Prince. 1999. Adaptive fuzzy segmentation of magnetic resonance images. IEEE transactions on medical imaging 18 9 (1999) 737--752.","key":"e_1_3_2_1_11_1","DOI":"10.1109\/42.802752"},{"doi-asserted-by":"crossref","unstructured":"M. N. Ahmed S. M. Yamany N. Mohamed A. A. Farag and T. Moriarty. 2002. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE transactions on medical imaging 21 3 (2002) 193--199.  M. N. Ahmed S. M. Yamany N. Mohamed A. A. Farag and T. Moriarty. 2002. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE transactions on medical imaging 21 3 (2002) 193--199.","key":"e_1_3_2_1_12_1","DOI":"10.1109\/42.996338"},{"doi-asserted-by":"crossref","unstructured":"K. S. Chuang H. L. Tzeng S. Chen J. Wu and T. J. Chen. 2006. Fuzzy C-means clustering with spatial information for image Segmentation. Computerized medical imaging and graphics 30 1 (2006) 9--15.  K. S. Chuang H. L. Tzeng S. Chen J. Wu and T. J. Chen. 2006. Fuzzy C-means clustering with spatial information for image Segmentation. Computerized medical imaging and graphics 30 1 (2006) 9--15.","key":"e_1_3_2_1_13_1","DOI":"10.1016\/j.compmedimag.2005.10.001"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1016\/j.patrec.2013.04.021"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1002\/cem.2728"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1016\/j.asoc.2015.05.038"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.5555\/3162421.3162595"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1016\/j.neuroimage.2005.02.018"},{"doi-asserted-by":"crossref","unstructured":"W. M. Wells III W. E. L. Grimson R. Kikinis and F. A. Jolesz. 1996. Adaptive segmentation of MRI data. IEEE transactions on medical imaging 15 4 (1996) 429--442.  W. M. Wells III W. E. L. Grimson R. Kikinis and F. A. Jolesz. 1996. Adaptive segmentation of MRI data. IEEE transactions on medical imaging 15 4 (1996) 429--442.","key":"e_1_3_2_1_19_1","DOI":"10.1109\/42.511747"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.1109\/42.585758"},{"doi-asserted-by":"crossref","unstructured":"Y. Zhang M. Brady and S. Smith. 2001. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging 20 1 (2001) 45--57.  Y. Zhang M. Brady and S. Smith. 2001. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging 20 1 (2001) 45--57.","key":"e_1_3_2_1_21_1","DOI":"10.1109\/42.906424"},{"doi-asserted-by":"crossref","unstructured":"K. Van Leemput F. Maes D. Vandermeulen and P. Suetens. 1999. Automated model-based bias field correction of MR images of the brain. IEEE transactions on medical imaging 18 10 (1999) 885--896.  K. Van Leemput F. Maes D. Vandermeulen and P. Suetens. 1999. Automated model-based bias field correction of MR images of the brain. IEEE transactions on medical imaging 18 10 (1999) 885--896.","key":"e_1_3_2_1_22_1","DOI":"10.1109\/42.811268"},{"doi-asserted-by":"crossref","unstructured":"Chunming Li John C Gore and Christos Davatzikos. 2014. Multiplicative intrinsic component optimization (mico) for MRI bias field estimation and tissue segmentation. Magnetic resonance imaging 32 7 (2014) 913--923.  Chunming Li John C Gore and Christos Davatzikos. 2014. Multiplicative intrinsic component optimization (mico) for MRI bias field estimation and tissue segmentation. Magnetic resonance imaging 32 7 (2014) 913--923.","key":"e_1_3_2_1_23_1","DOI":"10.1016\/j.mri.2014.03.010"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_24_1","DOI":"10.1016\/j.sigpro.2009.03.014"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.1023\/A:1014080923068"}],"event":{"sponsor":["University of Electronic Science and Technology of China University of Electronic Science and Technology of China"],"acronym":"ISICDM 2018","name":"ISICDM 2018: The 2nd International Symposium on Image Computing and Digital Medicine 2018","location":"Chengdu China"},"container-title":["Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3285996.3285997","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3285996.3285997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:44:15Z","timestamp":1750207455000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3285996.3285997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,13]]},"references-count":25,"alternative-id":["10.1145\/3285996.3285997","10.1145\/3285996"],"URL":"https:\/\/doi.org\/10.1145\/3285996.3285997","relation":{},"subject":[],"published":{"date-parts":[[2018,10,13]]},"assertion":[{"value":"2018-10-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}