{"id":"https://openalex.org/W2283221602","doi":"https://doi.org/10.1080/00207160.2015.1079625","title":"An improved hybrid gradient variation level set method for image segmentation and bias correction","display_name":"An improved hybrid gradient variation level set method for image segmentation and bias correction","publication_year":2015,"publication_date":"2015-08-04","ids":{"openalex":"https://openalex.org/W2283221602","doi":"https://doi.org/10.1080/00207160.2015.1079625","mag":"2283221602"},"language":"en","primary_location":{"id":"doi:10.1080/00207160.2015.1079625","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207160.2015.1079625","pdf_url":null,"source":{"id":"https://openalex.org/S124867444","display_name":"International Journal of Computer Mathematics","issn_l":"0020-7160","issn":["0020-7160","1026-7425","1029-0265"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Mathematics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050989091","display_name":"Junfeng Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfeng Cao","raw_affiliation_strings":["School of Science, Jiangnan University, Wuxi 214122, Jiangsu Province, China","School of IoT Engineering, Jiangnan University, Wuxi 214122, Jiangsu Province, China"],"affiliations":[{"raw_affiliation_string":"School of Science, Jiangnan University, Wuxi 214122, Jiangsu Province, China","institution_ids":["https://openalex.org/I111599522"]},{"raw_affiliation_string":"School of IoT Engineering, Jiangnan University, Wuxi 214122, Jiangsu Province, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087450445","display_name":"Xiao\u2010Jun Wu","orcid":"https://orcid.org/0000-0002-0310-5778"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaojun Wu","raw_affiliation_strings":["School of IoT Engineering, Jiangnan University, Wuxi 214122, Jiangsu Province, China"],"affiliations":[{"raw_affiliation_string":"School of IoT Engineering, Jiangnan University, Wuxi 214122, Jiangsu Province, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075083266","display_name":"He-Feng Yin","orcid":"https://orcid.org/0000-0001-5831-1475"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hefeng Yin","raw_affiliation_strings":["School of IoT Engineering, Jiangnan University, Wuxi 214122, Jiangsu Province, China"],"affiliations":[{"raw_affiliation_string":"School of IoT Engineering, Jiangnan University, Wuxi 214122, Jiangsu Province, China","institution_ids":["https://openalex.org/I111599522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087450445"],"corresponding_institution_ids":["https://openalex.org/I111599522"],"apc_list":null,"apc_paid":null,"fwci":0.3682,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.70078054,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"93","issue":"11","first_page":"1886","last_page":"1898"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11245","display_name":"Advanced Numerical Analysis Techniques","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/energy-functional","display_name":"Energy functional","score":0.8176019191741943},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.7381486892700195},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6973986029624939},{"id":"https://openalex.org/keywords/sobolev-space","display_name":"Sobolev space","score":0.5927865505218506},{"id":"https://openalex.org/keywords/balanced-flow","display_name":"Balanced flow","score":0.5919210910797119},{"id":"https://openalex.org/keywords/level-set","display_name":"Level set (data structures)","score":0.5307894945144653},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.49861812591552734},{"id":"https://openalex.org/keywords/gradient-method","display_name":"Gradient method","score":0.4847685694694519},{"id":"https://openalex.org/keywords/active-contour-model","display_name":"Active contour model","score":0.4402901828289032},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.42863136529922485},{"id":"https://openalex.org/keywords/level-set-method","display_name":"Level set method","score":0.4236922860145569},{"id":"https://openalex.org/keywords/partial-differential-equation","display_name":"Partial differential equation","score":0.41640493273735046},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.41485047340393066},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4082106351852417},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.3979164958000183},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.39338526129722595},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.39114993810653687},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.38926106691360474},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.1919969618320465},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16794130206108093},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14247927069664001}],"concepts":[{"id":"https://openalex.org/C191640071","wikidata":"https://www.wikidata.org/wiki/Q5377056","display_name":"Energy functional","level":2,"score":0.8176019191741943},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.7381486892700195},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6973986029624939},{"id":"https://openalex.org/C99730327","wikidata":"https://www.wikidata.org/wiki/Q1501536","display_name":"Sobolev space","level":2,"score":0.5927865505218506},{"id":"https://openalex.org/C167879884","wikidata":"https://www.wikidata.org/wiki/Q727568","display_name":"Balanced flow","level":2,"score":0.5919210910797119},{"id":"https://openalex.org/C153008295","wikidata":"https://www.wikidata.org/wiki/Q6535093","display_name":"Level set (data structures)","level":2,"score":0.5307894945144653},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.49861812591552734},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.4847685694694519},{"id":"https://openalex.org/C112353826","wikidata":"https://www.wikidata.org/wiki/Q127313","display_name":"Active contour model","level":4,"score":0.4402901828289032},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.42863136529922485},{"id":"https://openalex.org/C125269122","wikidata":"https://www.wikidata.org/wiki/Q1751397","display_name":"Level set method","level":4,"score":0.4236922860145569},{"id":"https://openalex.org/C93779851","wikidata":"https://www.wikidata.org/wiki/Q271977","display_name":"Partial differential equation","level":2,"score":0.41640493273735046},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.41485047340393066},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4082106351852417},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.3979164958000183},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.39338526129722595},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.39114993810653687},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.38926106691360474},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.1919969618320465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16794130206108093},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14247927069664001},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/00207160.2015.1079625","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207160.2015.1079625","pdf_url":null,"source":{"id":"https://openalex.org/S124867444","display_name":"International Journal of Computer Mathematics","issn_l":"0020-7160","issn":["0020-7160","1026-7425","1029-0265"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Mathematics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9100000262260437,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G73634308","display_name":null,"funder_award_id":"11202084","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1483726821","https://openalex.org/W1975935311","https://openalex.org/W1979393293","https://openalex.org/W1986640343","https://openalex.org/W1989135034","https://openalex.org/W1991113069","https://openalex.org/W2005424083","https://openalex.org/W2024143114","https://openalex.org/W2045582943","https://openalex.org/W2065350495","https://openalex.org/W2066657663","https://openalex.org/W2067821692","https://openalex.org/W2078952045","https://openalex.org/W2081727580","https://openalex.org/W2086447102","https://openalex.org/W2089838091","https://openalex.org/W2090903445","https://openalex.org/W2091868781","https://openalex.org/W2095049733","https://openalex.org/W2112891016","https://openalex.org/W2116040950","https://openalex.org/W2122184585","https://openalex.org/W2127401436","https://openalex.org/W2132116135","https://openalex.org/W2165734775"],"related_works":["https://openalex.org/W2367921179","https://openalex.org/W2116690004","https://openalex.org/W2095049733","https://openalex.org/W2132363464","https://openalex.org/W2990145420","https://openalex.org/W1989828405","https://openalex.org/W2102913354","https://openalex.org/W2076397479","https://openalex.org/W2161232058","https://openalex.org/W2149135821"],"abstract_inverted_index":{"Minimizing":[0],"an":[1,27],"energy":[2,28,38,109,119],"functional":[3,29,39,60],"defined":[4],"on":[5,88,140],"the":[6,42,57,62,66,96,103,107,117,121,146],"level":[7,17],"set":[8,18],"function":[9],"with":[10,134],"gradient":[11,44,51,64,67,78,105,115],"descent":[12,68],"method":[13,89,150],"is":[14,52],"called":[15],"variational":[16,21],"methods.":[19],"The":[20,49,125],"methods":[22],"are":[23],"characterized":[24],"by":[25,40,65],"deriving":[26],"from":[30],"some":[31],"priori":[32],"mathematical":[33],"models":[34],"and":[35,112,130,142],"minimizing":[36,56],"this":[37,71],"calculating":[41],"L2":[43,63,114],"over":[45],"all":[46],"possible":[47],"partitions.":[48],"Sobolev":[50,104],"more":[53],"effective":[54],"for":[55,98,106,116],"curve":[58],"length":[59],"than":[61],"method.":[69],"In":[70],"paper,":[72],"we":[73],"propose":[74],"a":[75],"novel":[76],"hybrid":[77],"active":[79],"contour":[80],"model":[81,127],"in":[82,151],"partial":[83],"differential":[84],"equation":[85],"formulation":[86],"based":[87],"of":[90,123,148,153],"Li":[91],"et":[92],"al.":[93],"to":[94],"correct":[95],"bias":[97],"image":[99],"segmentation.":[100],"By":[101],"using":[102,113],"internal":[108],"(curve":[110],"length),":[111],"external":[118],"during":[120],"evolution":[122],"curve.":[124],"proposed":[126],"can":[128],"effectively":[129],"efficiently":[131],"segment":[132],"images":[133,144],"intensity":[135],"inhomogeneity.":[136],"Experimental":[137],"results":[138],"obtained":[139],"synthetic":[141],"real":[143],"show":[145],"advantages":[147],"our":[149],"terms":[152],"computational":[154],"efficiency.":[155]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
