{"id":"https://openalex.org/W6888452894","doi":"https://doi.org/10.21227/jvz7-r769","title":"Original images of colloidal gold immunochromatographic strip","display_name":"Original images of colloidal gold immunochromatographic strip","publication_year":2019,"publication_date":"2019-11-16","ids":{"openalex":"https://openalex.org/W6888452894","doi":"https://doi.org/10.21227/jvz7-r769"},"language":"en","primary_location":{"id":"doi:10.21227/jvz7-r769","is_oa":true,"landing_page_url":"https://doi.org/10.21227/jvz7-r769","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/jvz7-r769","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Guo, Wensheng","orcid":"https://orcid.org/0000-0002-9462-2526"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Wensheng","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-9462-2526","affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Yang, Hai","orcid":"https://orcid.org/0000-0003-2426-7300"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Hai","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2426-7300","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5497000217437744},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5235000252723694},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4715000092983246},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.460099995136261},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.42419999837875366},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4194999933242798},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.41359999775886536}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6542999744415283},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5497000217437744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5486000180244446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5293999910354614},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5235000252723694},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4715000092983246},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.460099995136261},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4027000069618225},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C182124507","wikidata":"https://www.wikidata.org/wiki/Q166154","display_name":"Line segment","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C62469222","wikidata":"https://www.wikidata.org/wiki/Q17092103","display_name":"Hybrid algorithm (constraint satisfaction)","level":5,"score":0.30970001220703125},{"id":"https://openalex.org/C206824153","wikidata":"https://www.wikidata.org/wiki/Q1169834","display_name":"Region growing","level":5,"score":0.3012000024318695},{"id":"https://openalex.org/C3017489831","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Running time","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2687000036239624},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.2660999894142151},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/jvz7-r769","is_oa":true,"landing_page_url":"https://doi.org/10.21227/jvz7-r769","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/jvz7-r769","is_oa":true,"landing_page_url":"https://doi.org/10.21227/jvz7-r769","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,161],"of":[2,24,162,175],"test":[3],"line":[4,7],"and":[5,37,54,79,98,107,153,181,203],"control":[6],"for":[8,29,68],"colloidal":[9],"gold":[10],"immunochromatographic":[11],"strip":[12],"(GICS)":[13],"images":[14,174,193],"with":[15,91,104,198],"image":[16,31],"processing":[17],"algorithms":[18],"is":[19,66,134,169],"essential":[20],"to":[21,60,96,171],"quantitative":[22],"analysis":[23],"GICS.":[25],"As":[26],"common":[27],"methods":[28],"GICS":[30,101,173],"segmentation,":[32],"fuzzy":[33],"c-means":[34],"(FCM)":[35],"algorithm":[36,42,89,106,112,121,148,168,180,183,200],"cellular":[38],"neural":[39],"network":[40],"(CNN)":[41],"both":[43],"require":[44],"presetting":[45,195],"initial":[46,49,157,196],"conditions":[47],"(specifying":[48],"parameters":[50],"or":[51],"training":[52],"models)":[53],"take":[55],"long":[56],"running":[57,201],"time,":[58,202],"due":[59],"high":[61],"calculation":[62,124],"cost.":[63],"Therefore":[64],"neither":[65],"ideal":[67],"a":[69,86,135,140],"point-of-care":[70],"testing":[71],"(POCT)":[72],"device,":[73,131],"which":[74,126],"has":[75,113],"low":[76,123],"hardware":[77],"cost":[78],"limited":[80],"computing":[81],"power.":[82],"This":[83],"paper":[84],"designs":[85],"region":[87,119],"growing":[88,120,154],"combined":[90],"fast":[92,145],"peak":[93,146],"detection":[94,147],"(RGFPD)":[95],"quickly":[97],"self-adaptively":[99],"segment":[100,172,192],"images.":[102,163],"Compared":[103],"FCM":[105,179],"CNN":[108,182],"algorithm,":[109,137],"the":[110,118,129,144,150],"RGFPD":[111,167,190],"two":[114],"obvious":[115],"advantages.":[116],"First,":[117],"requires":[122],"cost,":[125],"better":[127,205],"suits":[128],"POCT":[130],"because":[132],"it":[133],"local":[136],"rather":[138],"than":[139],"global":[141],"one.":[142],"Second,":[143],"calculates":[149],"seed":[151],"points":[152],"criterions":[155],"as":[156,184],"conditions,":[158,197],"realizing":[159],"self-adaptive":[160],"In":[164],"this":[165],"paper,":[166],"applied":[170],"actual":[176],"samples,":[177],"taking":[178],"contrast.":[185],"The":[186],"results":[187],"show":[188],"that":[189],"accurately":[191],"without":[194],"shorter":[199],"performs":[204],"in":[206],"anti-interference.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}