{"id":"https://openalex.org/W2765331251","doi":"https://doi.org/10.1145/3129676.3129701","title":"Vessel Segmentation Model using Automated Threshold Algorithm from Lower Leg MRI","display_name":"Vessel Segmentation Model using Automated Threshold Algorithm from Lower Leg MRI","publication_year":2017,"publication_date":"2017-09-20","ids":{"openalex":"https://openalex.org/W2765331251","doi":"https://doi.org/10.1145/3129676.3129701","mag":"2765331251"},"language":"en","primary_location":{"id":"doi:10.1145/3129676.3129701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3129676.3129701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","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/A5085227020","display_name":"Ji Young Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ji Young Lee","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA","institution_ids":["https://openalex.org/I177156846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008470746","display_name":"Jin Yeong Mun","orcid":null},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin Yeong Mun","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA","institution_ids":["https://openalex.org/I177156846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023391264","display_name":"Mohammad Taheri","orcid":"https://orcid.org/0000-0003-4888-5883"},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Taheri","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA","institution_ids":["https://openalex.org/I177156846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054716722","display_name":"Seong\u2010Ho Son","orcid":"https://orcid.org/0000-0003-1343-1806"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seong Ho Son","raw_affiliation_strings":["Electronics and Telecommunications Research Institute, DaeJeon, South Korea"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute, DaeJeon, South Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034719381","display_name":"Sung Y. Shin","orcid":"https://orcid.org/0000-0002-2832-5208"},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sung Shin","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA","institution_ids":["https://openalex.org/I177156846"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5085227020"],"corresponding_institution_ids":["https://openalex.org/I177156846"],"apc_list":null,"apc_paid":null,"fwci":0.8331,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.74537344,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"120","last_page":"125"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9909999966621399,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9735999703407288,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/segmentation","display_name":"Segmentation","score":0.7637388706207275},{"id":"https://openalex.org/keywords/hough-transform","display_name":"Hough transform","score":0.674807071685791},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6556676626205444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6262406706809998},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5470751523971558},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5388038158416748},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5045758485794067},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4773854613304138},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4746066927909851},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.4525165557861328},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.20358410477638245},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14534997940063477},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14220058917999268}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7637388706207275},{"id":"https://openalex.org/C200518788","wikidata":"https://www.wikidata.org/wiki/Q195076","display_name":"Hough transform","level":3,"score":0.674807071685791},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6556676626205444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6262406706809998},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5470751523971558},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5388038158416748},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5045758485794067},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4773854613304138},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4746066927909851},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.4525165557861328},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.20358410477638245},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14534997940063477},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14220058917999268},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3129676.3129701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3129676.3129701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1547714734","https://openalex.org/W1967124425","https://openalex.org/W1974954013","https://openalex.org/W1977967056","https://openalex.org/W2006594119","https://openalex.org/W2008993105","https://openalex.org/W2009379587","https://openalex.org/W2028575667","https://openalex.org/W2032290571","https://openalex.org/W2054212355","https://openalex.org/W2090378746","https://openalex.org/W2113049544","https://openalex.org/W2115680416","https://openalex.org/W2117661433","https://openalex.org/W2122374500","https://openalex.org/W2122967651","https://openalex.org/W2148358298","https://openalex.org/W2158050435","https://openalex.org/W2163627070","https://openalex.org/W2167204126","https://openalex.org/W2184413074","https://openalex.org/W2294434537","https://openalex.org/W2488305974"],"related_works":["https://openalex.org/W2030098947","https://openalex.org/W1974777989","https://openalex.org/W2003466055","https://openalex.org/W2363834444","https://openalex.org/W2070077862","https://openalex.org/W2765199790","https://openalex.org/W2164944168","https://openalex.org/W2326760703","https://openalex.org/W262984167","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Blood":[0],"vessel":[1,50,72],"segmentation":[2,51,107],"has":[3,146,168],"been":[4,147],"developed":[5],"in":[6,26,45,52,73,114,123,139],"the":[7,30,46,74,124,136,169,172],"liver,":[8],"heart,":[9],"and":[10,17,36,99,118],"retinal":[11],"images":[12],"due":[13],"to":[14,42],"accurate":[15],"description":[16],"analysis":[18],"of":[19],"vascular":[20,43],"structure":[21,44],"plays":[22],"a":[23,64,155,187],"crucial":[24],"role":[25],"clinical":[27],"routine.":[28],"Since":[29],"varicose":[31],"vein,":[32],"deep":[33],"vein":[34],"thrombosis,":[35],"occlusive":[37],"arterial":[38],"diseases":[39],"are":[40],"related":[41],"lower":[47,53,75],"leg,":[48],"blood":[49],"limbs":[54],"is":[55,84,106,132,176],"also":[56],"clinically":[57],"important.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62],"proposed":[63,82,144,166],"feature-based":[65],"adaptive":[66],"threshold":[67,119,128],"model":[68,83,145,167],"for":[69,149],"automatically":[70],"extracting":[71],"leg":[76],"Magnetic":[77],"Resonance":[78],"Images":[79],"(MRIs).":[80],"The":[81,89,103,143],"divided":[85],"into":[86],"2":[87],"stages.":[88],"first":[90],"stage,":[91],"pre-processing,":[92],"included":[93,122],"partial":[94],"volume":[95],"reduction,":[96],"contrast":[97],"equalization,":[98],"removing":[100],"background":[101],"noises.":[102],"second":[104,125],"stage":[105],"stage.":[108,126],"Fuzzy":[109],"C-mean":[110],"clustering,":[111],"Hough":[112,137],"transform":[113,138],"feature":[115,140],"extraction":[116,141],"technique,":[117],"algorithm":[120,131],"were":[121],"Automatic":[127],"value":[129],"determination":[130],"enhanced":[133],"by":[134],"using":[135],"technique.":[142],"implemented":[148],"showing":[150],"accuracy":[151,170],"(ACC)":[152],"compared":[153],"with":[154,171],"manually":[156],"generated":[157],"ground":[158],"truth":[159],"from":[160],"domain":[161],"experts.":[162],"Results":[163],"show":[164],"that":[165],"average":[173],"98.43%,":[174],"which":[175],"higher":[177],"than":[178],"existing":[179],"model,":[180],"Adaptive":[181],"Vein":[182],"Segmentation":[183],"(AVS)":[184],"method":[185],"as":[186],"reference":[188],"[1].":[189]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
