{"id":"https://openalex.org/W2020093041","doi":"https://doi.org/10.1109/healthcom.2012.6379408","title":"An enhanced threshold based technique for white blood cells nuclei automatic segmentation","display_name":"An enhanced threshold based technique for white blood cells nuclei automatic segmentation","publication_year":2012,"publication_date":"2012-10-01","ids":{"openalex":"https://openalex.org/W2020093041","doi":"https://doi.org/10.1109/healthcom.2012.6379408","mag":"2020093041"},"language":"en","primary_location":{"id":"doi:10.1109/healthcom.2012.6379408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom.2012.6379408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom)","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/A5101934458","display_name":"Mostafa M. Mohamed","orcid":"https://orcid.org/0000-0002-9086-0180"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Mostafa Mohamed","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada","Department of Electrical & Computer Engineering, University of Calgary, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]},{"raw_affiliation_string":"Department of Electrical & Computer Engineering, University of Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008779348","display_name":"Behrouz H. Far","orcid":"https://orcid.org/0000-0003-1589-8039"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Behrouz Far","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada","Department of Electrical & Computer Engineering, University of Calgary, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]},{"raw_affiliation_string":"Department of Electrical & Computer Engineering, University of Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101934458"],"corresponding_institution_ids":["https://openalex.org/I168635309"],"apc_list":null,"apc_paid":null,"fwci":2.4709,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.90159914,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"202","last_page":"207"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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/T10862","display_name":"AI in cancer detection","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9491000175476074,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7553840279579163},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7448575496673584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6807061433792114},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6377374529838562},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5023961067199707},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48465830087661743},{"id":"https://openalex.org/keywords/matlab","display_name":"MATLAB","score":0.42389464378356934},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.41415727138519287}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7553840279579163},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7448575496673584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6807061433792114},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6377374529838562},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5023961067199707},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48465830087661743},{"id":"https://openalex.org/C2780365114","wikidata":"https://www.wikidata.org/wiki/Q169478","display_name":"MATLAB","level":2,"score":0.42389464378356934},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.41415727138519287},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/healthcom.2012.6379408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom.2012.6379408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310597","display_name":"Calgary Laboratory Services","ror":"https://ror.org/00g6ztp19"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W155685019","https://openalex.org/W598096340","https://openalex.org/W1426511931","https://openalex.org/W1564419782","https://openalex.org/W1601107692","https://openalex.org/W1855906352","https://openalex.org/W1971819012","https://openalex.org/W1993127575","https://openalex.org/W2020619173","https://openalex.org/W2032673367","https://openalex.org/W2093075920","https://openalex.org/W2101928387","https://openalex.org/W2102699116","https://openalex.org/W2105034489","https://openalex.org/W2105493237","https://openalex.org/W2138398901","https://openalex.org/W2144615342","https://openalex.org/W2168175388","https://openalex.org/W4294305479","https://openalex.org/W6606231175","https://openalex.org/W6617926443","https://openalex.org/W6990029666"],"related_works":["https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W3011384228","https://openalex.org/W2945274617","https://openalex.org/W3199300986","https://openalex.org/W4313052709","https://openalex.org/W4298131179","https://openalex.org/W2375430703"],"abstract_inverted_index":{"One":[0],"of":[1,117],"the":[2,9,62,92,118,123,135,165,175,185],"most":[3,63],"important":[4,64],"clinical":[5,14],"examination":[6],"tests":[7],"is":[8,20,25,70,86,170],"blood":[10,18,80,106,124,149,181],"test.":[11],"In":[12,130],"a":[13,45],"laboratory,":[15],"counting":[16],"different":[17],"cells":[19,150],"important.":[21],"Manual":[22],"microscopic":[23],"inspection":[24],"time-consuming":[26],"and":[27,47,60,90,172,184,197],"requires":[28],"technical":[29],"knowledge.":[30],"Therefore,":[31],"automatic":[32,52,67,79],"medical":[33],"diagnosis":[34],"systems":[35],"are":[36,99,188],"required":[37],"to":[38,41,110,132,142,156,163],"help":[39],"physicians":[40],"diagnose":[42],"diseases":[43],"in":[44,66],"fast":[46],"yet":[48],"efficient":[49,76],"way.":[50],"Cell":[51],"classification":[53,68],"has":[54],"larger":[55],"interest":[56],"especially":[57],"for":[58,78,154,195],"clinics":[59],"laboratories;":[61],"step":[65],"success":[69],"segmentation.":[71,83],"This":[72,84],"paper":[73],"shows":[74],"an":[75],"technique":[77,85,121,138],"cell":[81],"nuclei":[82],"relying":[87],"on":[88,122,190],"enhancing":[89],"filtering":[91],"gray":[93],"scale":[94],"image":[95,125],"contrast.":[96],"False":[97],"objects":[98],"removed":[100],"utilizing":[101],"minimum":[102],"segment":[103],"size.":[104],"365":[105],"images":[107,182],"were":[108,152],"used":[109,153],"examine":[111],"this":[112],"segmentation":[113,120,137,167],"technique.":[114],"Quantitative":[115],"analysis":[116],"proposed":[119,136],"set":[126],"gives":[127],"80.6%":[128],"accuracy.":[129],"comparison":[131,196],"other":[133],"techniques":[134],"performance":[139],"was":[140,161,174],"found":[141,162],"be":[143],"superior.":[144],"The":[145,180],"five":[146],"normal":[147],"white":[148],"types":[151],"evaluation":[155],"compare":[157],"isolated":[158],"performance.":[159],"Eosinophil":[160],"have":[164],"lowest":[166],"accuracy":[168],"which":[169],"71.0%":[171],"Monocyte":[173],"highest":[176],"one":[177],"with":[178],"85.9%.":[179],"dataset":[183],"source":[186],"code":[187],"published":[189],"MATLAB":[191],"file":[192],"exchange":[193],"website":[194],"re-production.":[198]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":3}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}
