{"id":"https://openalex.org/W2408678574","doi":"https://doi.org/10.1109/icip.2016.7532807","title":"Gabor barcodes for medical image retrieval","display_name":"Gabor barcodes for medical image retrieval","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2408678574","doi":"https://doi.org/10.1109/icip.2016.7532807","mag":"2408678574"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532807","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1605.04478","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mina Nouredanesh","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Mina Nouredanesh","raw_affiliation_strings":["Department of Mechanical and Mechatronics Engineering, University of Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Mechatronics Engineering, University of Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hamid R. Tizhoosh","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hamid R. Tizhoosh","raw_affiliation_strings":["KIMIA Lab., University of Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"KIMIA Lab., University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ershad Banijamali","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ershad Banijamali","raw_affiliation_strings":["Cheriton School of Computer Science, University of Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Cheriton School of Computer Science, University of Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":0.6766,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.76662468,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2490","last_page":"2493"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9986000061035156,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9986000061035156,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9950000047683716,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9876999855041504,"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/robustness","display_name":"Robustness (evolution)","score":0.66839998960495},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.6495000123977661},{"id":"https://openalex.org/keywords/barcode","display_name":"Barcode","score":0.6193000078201294},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5867999792098999},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5699999928474426},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43639999628067017},{"id":"https://openalex.org/keywords/gabor-filter","display_name":"Gabor filter","score":0.4230000078678131},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41530001163482666}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.771399974822998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6700999736785889},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.66839998960495},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.6495000123977661},{"id":"https://openalex.org/C2776841711","wikidata":"https://www.wikidata.org/wiki/Q856","display_name":"Barcode","level":2,"score":0.6193000078201294},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6018999814987183},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5699999928474426},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43639999628067017},{"id":"https://openalex.org/C2779883129","wikidata":"https://www.wikidata.org/wiki/Q2447890","display_name":"Gabor filter","level":3,"score":0.4230000078678131},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.40939998626708984},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.3889000117778778},{"id":"https://openalex.org/C197231052","wikidata":"https://www.wikidata.org/wiki/Q979829","display_name":"Radon transform","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C173149727","wikidata":"https://www.wikidata.org/wiki/Q996397","display_name":"Gabor transform","level":4,"score":0.3418000042438507},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C193828747","wikidata":"https://www.wikidata.org/wiki/Q864118","display_name":"Binary image","level":4,"score":0.2896000146865845},{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.2696000039577484},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip.2016.7532807","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1605.04478","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.04478","pdf_url":"https://arxiv.org/pdf/1605.04478","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1605.04478","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.04478","pdf_url":"https://arxiv.org/pdf/1605.04478","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W783096245","https://openalex.org/W1797238555","https://openalex.org/W1966600465","https://openalex.org/W1969840923","https://openalex.org/W1975826132","https://openalex.org/W2065301447","https://openalex.org/W2091967951","https://openalex.org/W2112162870","https://openalex.org/W2126440645","https://openalex.org/W2129749203","https://openalex.org/W2156648016","https://openalex.org/W2162018804","https://openalex.org/W2172177969","https://openalex.org/W2185793836","https://openalex.org/W2202393049","https://openalex.org/W2259336176","https://openalex.org/W4237397995","https://openalex.org/W6677724871","https://openalex.org/W6991965754"],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"advances":[3],"in":[4,120,167],"medical":[5,75],"imaging":[6],"have":[7,52,103],"led":[8],"to":[9,61,97],"the":[10,27,33,47,67,134,139,148,181,212],"emergence":[11],"of":[12,21,32,66,74,150,160,188],"massive":[13],"databases,":[14],"containing":[15],"images":[16,34,76,191,197],"from":[17,84],"a":[18,58,94,130,144],"diverse":[19],"range":[20],"modalities.":[22],"This":[23,123],"has":[24,177],"significantly":[25],"heightened":[26],"need":[28],"for":[29,72,133,143,192,198,211],"automated":[30],"annotation":[31],"on":[35,46,180],"one":[36],"side,":[37],"and":[38,40,109,117,162,171,194],"fast":[39],"memory-efficient":[41],"content-based":[42],"image":[43,118,135],"retrieval":[44,172],"systems":[45],"other":[48],"side.":[49],"Binary":[50],"descriptors":[51,71],"recently":[53,68],"gained":[54],"more":[55],"attention":[56],"as":[57,114,129,204,206],"potential":[59],"vehicle":[60],"achieve":[62],"these":[63],"goals.":[64],"One":[65],"introduced":[69],"binary":[70],"tagging":[73],"are":[77,82,164],"Radon":[78,85],"barcodes":[79],"(RBCs)":[80],"that":[81],"driven":[83],"transform":[86,91,96],"via":[87],"local":[88],"thresholding.":[89],"Gabor":[90,101,126,152],"is":[92],"also":[93,110],"powerful":[95],"extract":[98],"texture-based":[99],"information.":[100],"features":[102],"exhibited":[104],"robustness":[105],"against":[106],"rotation,":[107],"scale,":[108],"photometric":[111],"disturbances,":[112],"such":[113],"illumination":[115],"changes":[116],"noise":[119],"many":[121],"applications.":[122],"paper":[124],"introduces":[125],"Barcodes":[127],"(GBCs),":[128],"novel":[131],"framework":[132],"annotation.":[136],"To":[137],"find":[138],"most":[140],"discriminative":[141],"GBC":[142],"given":[145],"query":[146],"image,":[147],"effects":[149],"employing":[151],"filters":[153],"with":[154,184],"different":[155,158,168],"parameters,":[156],"i.e.,":[157],"sets":[159],"scales":[161],"orientations,":[163],"investigated,":[165],"resulting":[166],"barcode":[169],"lengths":[170],"performances.":[173],"The":[174],"proposed":[175],"method":[176],"been":[178],"evaluated":[179],"IRMA":[182],"dataset":[183],"193":[185],"classes":[186],"comprising":[187],"12,677":[189],"x-ray":[190],"indexing,":[193],"1,733":[195],"x-rays":[196],"testing.":[199],"A":[200],"total":[201],"error":[202],"score":[203],"low":[205],"351":[207],"(\u2248":[208],"80%":[209],"accuracy":[210],"first":[213],"hit)":[214],"was":[215],"achieved.":[216]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2016-06-24T00:00:00"}