{"id":"https://openalex.org/W2998125717","doi":"https://doi.org/10.1145/3364836.3364861","title":"Accurate and Robust Active Contour Model for Medical Image Segmentation and Correction","display_name":"Accurate and Robust Active Contour Model for Medical Image Segmentation and Correction","publication_year":2019,"publication_date":"2019-08-24","ids":{"openalex":"https://openalex.org/W2998125717","doi":"https://doi.org/10.1145/3364836.3364861","mag":"2998125717"},"language":"en","primary_location":{"id":"doi:10.1145/3364836.3364861","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","raw_type":"proceedings-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/A5090906323","display_name":"Yunyun Yang","orcid":"https://orcid.org/0000-0002-0488-7652"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunyun Yang","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077944339","display_name":"Yunna Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunna Yang","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090906323"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1589515,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"123","last_page":"127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9926999807357788,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6657555103302002},{"id":"https://openalex.org/keywords/active-contour-model","display_name":"Active contour model","score":0.643620491027832},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6434177160263062},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6415045857429504},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6120361685752869},{"id":"https://openalex.org/keywords/energy-functional","display_name":"Energy functional","score":0.5933228135108948},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5773838758468628},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5284999012947083},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4866967499256134},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4533209502696991},{"id":"https://openalex.org/keywords/level-set","display_name":"Level set (data structures)","score":0.4409292936325073},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43866705894470215},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.4199894070625305},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24021977186203003},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08959487080574036}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6657555103302002},{"id":"https://openalex.org/C112353826","wikidata":"https://www.wikidata.org/wiki/Q127313","display_name":"Active contour model","level":4,"score":0.643620491027832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6434177160263062},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6415045857429504},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6120361685752869},{"id":"https://openalex.org/C191640071","wikidata":"https://www.wikidata.org/wiki/Q5377056","display_name":"Energy functional","level":2,"score":0.5933228135108948},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5773838758468628},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5284999012947083},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4866967499256134},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4533209502696991},{"id":"https://openalex.org/C153008295","wikidata":"https://www.wikidata.org/wiki/Q6535093","display_name":"Level set (data structures)","level":2,"score":0.4409292936325073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43866705894470215},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.4199894070625305},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24021977186203003},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08959487080574036},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3364836.3364861","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8700000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1989532447","https://openalex.org/W2055692680","https://openalex.org/W2080668963","https://openalex.org/W2116040950","https://openalex.org/W2132116135","https://openalex.org/W2139478903","https://openalex.org/W2142058898","https://openalex.org/W2564011781","https://openalex.org/W2792176849","https://openalex.org/W2793109148","https://openalex.org/W2887667162"],"related_works":["https://openalex.org/W2368150111","https://openalex.org/W2125739208","https://openalex.org/W2098360446","https://openalex.org/W2299318636","https://openalex.org/W2052479847","https://openalex.org/W2135353231","https://openalex.org/W2391867215","https://openalex.org/W2095049733","https://openalex.org/W2807700654","https://openalex.org/W2404178732"],"abstract_inverted_index":{"Magnetic":[0],"resonance":[1,12],"imaging":[2],"has":[3],"developed":[4],"at":[5],"an":[6,44,99],"extremely":[7],"rapid":[8],"rate":[9],"and":[10,39,46,98,114,167,177,197],"magnetic":[11],"(MR)":[13],"images":[14,27,140,154],"can":[15,151],"provide":[16],"a":[17,69,89,95],"lot":[18],"of":[19,58,77,137,189,194],"information":[20],"for":[21,51],"doctors":[22],"to":[23,125,135,141,170],"diagnose.":[24],"However,":[25],"MR":[26,52,139],"always":[28],"suffer":[29],"from":[30],"intensity":[31],"inhomogeneity":[32],"which":[33],"may":[34],"cause":[35],"difficulty":[36],"in":[37,111,192],"segmentation":[38,195],"analysis.":[40],"This":[41],"paper":[42],"presents":[43],"accurate":[45],"robust":[47,169],"active":[48],"contour":[49],"model":[50,80,110,134,150,176,184,191],"images.":[53],"Inspired":[54],"by":[55,72,93],"the":[56,59,74,78,82,104,128,174,178,187],"idea":[57],"multiplicative":[60],"intrinsic":[61],"component":[62],"optimization":[63],"(MICO)":[64],"model,":[65],"we":[66,87],"first":[67],"define":[68,88],"data":[70,105],"term":[71,97],"transforming":[73],"energy":[75,91,129],"functional":[76,92],"MICO":[79,175],"into":[81,103],"level":[83,116],"set":[84,117],"framework.":[85],"Then,":[86],"new":[90],"incorporating":[94],"length":[96],"edge":[100],"detector":[101],"function":[102],"term.":[106],"We":[107,131],"present":[108],"our":[109,133,149,190],"both":[112],"two-phase":[113],"four-phase":[115],"formulations.":[118],"The":[119],"split":[120],"Bregman":[121],"method":[122],"is":[123,168],"applied":[124],"efficiently":[126],"minimize":[127],"functionals.":[130],"apply":[132],"lots":[136],"brain":[138],"test":[142],"its":[143],"performance.":[144],"Experimental":[145],"results":[146],"show":[147],"that":[148],"handle":[152],"these":[153],"well":[155],"even":[156],"if":[157],"they":[158],"are":[159],"polluted":[160],"with":[161,173],"serious":[162],"bias":[163],"field":[164],"or":[165],"shadows":[166],"noises.":[171],"Comparison":[172],"Coherent":[179],"Local":[180],"Intensity":[181],"Clustering":[182],"(CLIC)":[183],"also":[185],"demonstrates":[186],"superiority":[188],"terms":[193],"accuracy":[196],"correction":[198],"effect.":[199]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
