{"id":"https://openalex.org/W2921539660","doi":"https://doi.org/10.23919/apsipa.2018.8659682","title":"Improved Vessel Segmentation Using Curvelet Transform and Line Operators","display_name":"Improved Vessel Segmentation Using Curvelet Transform and Line Operators","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2921539660","doi":"https://doi.org/10.23919/apsipa.2018.8659682","mag":"2921539660"},"language":"en","primary_location":{"id":"doi:10.23919/apsipa.2018.8659682","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5007222940","display_name":"Renoh Johnson Chalakkal","orcid":"https://orcid.org/0000-0003-3452-5633"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Renoh Johnson Chalakkal","raw_affiliation_strings":["The University of Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"The University of Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043619920","display_name":"Waleed H. Abdulla","orcid":"https://orcid.org/0000-0002-1812-4285"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Waleed H. Abdulla","raw_affiliation_strings":["The University of Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"The University of Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007222940"],"corresponding_institution_ids":["https://openalex.org/I154130895"],"apc_list":null,"apc_paid":null,"fwci":1.2585,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.81450509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2041","last_page":"2046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":1.0,"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":1.0,"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":0.9959999918937683,"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/T10250","display_name":"Glaucoma and retinal disorders","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7816455960273743},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6880508065223694},{"id":"https://openalex.org/keywords/curvelet","display_name":"Curvelet","score":0.6878372430801392},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6595935821533203},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6522126197814941},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5890207290649414},{"id":"https://openalex.org/keywords/adaptive-histogram-equalization","display_name":"Adaptive histogram equalization","score":0.5382193922996521},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49738243222236633},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4662289619445801},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46334394812583923},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4621039628982544},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.45008793473243713},{"id":"https://openalex.org/keywords/fundus","display_name":"Fundus (uterus)","score":0.4209050238132477},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.41308358311653137},{"id":"https://openalex.org/keywords/histogram-equalization","display_name":"Histogram equalization","score":0.3585309088230133},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.21694877743721008},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1927201747894287}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7816455960273743},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6880508065223694},{"id":"https://openalex.org/C131720326","wikidata":"https://www.wikidata.org/wiki/Q5196075","display_name":"Curvelet","level":4,"score":0.6878372430801392},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6595935821533203},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6522126197814941},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5890207290649414},{"id":"https://openalex.org/C30387639","wikidata":"https://www.wikidata.org/wiki/Q4680744","display_name":"Adaptive histogram equalization","level":5,"score":0.5382193922996521},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49738243222236633},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4662289619445801},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46334394812583923},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4621039628982544},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.45008793473243713},{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.4209050238132477},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.41308358311653137},{"id":"https://openalex.org/C136943445","wikidata":"https://www.wikidata.org/wiki/Q1970240","display_name":"Histogram equalization","level":4,"score":0.3585309088230133},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.21694877743721008},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1927201747894287},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/apsipa.2018.8659682","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},{"id":"pmh:oai:researchspace.auckland.ac.nz:2292/46646","is_oa":false,"landing_page_url":"http://hdl.handle.net/2292/46646","pdf_url":null,"source":{"id":"https://openalex.org/S7407055463","display_name":"ResearchSpace (University of Auckland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I154130895","host_organization_name":"University of Auckland","host_organization_lineage":["https://openalex.org/I154130895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Item"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323893","display_name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","ror":"https://ror.org/05vna4324"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1973547747","https://openalex.org/W1978236354","https://openalex.org/W2001412060","https://openalex.org/W2005069713","https://openalex.org/W2010965043","https://openalex.org/W2033723371","https://openalex.org/W2054095810","https://openalex.org/W2060615126","https://openalex.org/W2090236240","https://openalex.org/W2105839704","https://openalex.org/W2109731900","https://openalex.org/W2111755266","https://openalex.org/W2115528090","https://openalex.org/W2115680416","https://openalex.org/W2116216752","https://openalex.org/W2116628223","https://openalex.org/W2132519114","https://openalex.org/W2134092095","https://openalex.org/W2145305441","https://openalex.org/W2150134853","https://openalex.org/W2150769593","https://openalex.org/W2158240273","https://openalex.org/W2164741953","https://openalex.org/W2170092083","https://openalex.org/W2171459511","https://openalex.org/W2294926082","https://openalex.org/W2404019942","https://openalex.org/W2433259561","https://openalex.org/W2568842486","https://openalex.org/W2594602371"],"related_works":["https://openalex.org/W2162593906","https://openalex.org/W2387482914","https://openalex.org/W2385217229","https://openalex.org/W2302472796","https://openalex.org/W2376822833","https://openalex.org/W2393164332","https://openalex.org/W4319781082","https://openalex.org/W2015245117","https://openalex.org/W2604503469","https://openalex.org/W2381972374"],"abstract_inverted_index":{"Vessel":[0],"segmentation":[1],"from":[2],"the":[3,28,42,65,80,85,88,91,95,122,127,136,143,153,163,171,176],"fundus":[4],"retinal":[5,92,128],"images":[6],"is":[7,98,132,158,173],"highly":[8],"significant":[9],"in":[10,27,74],"diagnosing":[11],"many":[12,25],"pathologies":[13],"related":[14],"to":[15,79,151,161],"eye":[16],"and":[17,59,94,115,191,199],"other":[18,81,177],"systemic":[19],"diseases.":[20],"Even":[21],"though":[22],"there":[23],"are":[24,38,138,201],"methods":[26,37,179],"literature":[29],"focusing":[30,40],"on":[31,41,56],"this":[32,48],"task,":[33],"most":[34],"of":[35,104,170,195],"these":[36],"not":[39],"small":[43,66],"peripheral":[44,67],"vessels":[45,68,93,137],"segmentation.":[46],"In":[47,84],"paper,":[49],"we":[50],"propose":[51],"a":[52,75,102,183],"new":[53],"approach":[54],"based":[55],"curvelet":[57,124],"transform":[58,125],"line":[60,144],"operators":[61],"which":[62],"can":[63],"segment":[64],"with":[69],"very":[70],"high":[71],"accuracy":[72,190],"resulting":[73],"higher":[76],"sensitivity":[77],"compared":[78,174],"state-of-the-art":[82,178],"methods.":[83],"proposed":[86],"approach,":[87],"contrast":[89,131],"between":[90],"background":[96,166],"pixels":[97],"enhanced":[99],"by":[100,120,141,148],"applying":[101,142],"series":[103],"image":[105],"processing":[106,157],"steps":[107],"involving":[108],"color":[109],"space":[110],"transformation,":[111],"adaptive":[112],"histogram":[113],"equalization,":[114],"anisotropic":[116],"diffusion":[117],"filtering.":[118],"Then":[119],"using":[121,180],"modified":[123],"coefficients,":[126],"vessel":[129],"edge":[130],"further":[133],"enhanced.":[134],"Finally,":[135],"segmented":[139,154],"out":[140,160],"operator":[145],"response,":[146],"followed":[147],"suitable":[149],"thresholding":[150],"obtain":[152],"vessels.":[155],"Post":[156],"carried":[159],"remove":[162],"scattered":[164],"unwanted":[165],"pixels.":[167],"The":[168],"performance":[169],"method":[172],"against":[175],"DRIVE":[181],"as":[182],"testing":[184],"database.":[185],"An":[186],"average":[187],"sensitivity,":[188],"specificity,":[189],"positive":[192],"predictive":[193],"value":[194],"0.7653,":[196],"0.9735,":[197],"0.9542":[198],"0.7438":[200],"respectively":[202],"achieved.":[203]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}