{"id":"https://openalex.org/W1973761609","doi":"https://doi.org/10.1145/2093698.2093742","title":"Unsupervised segmentation for MR brain images","display_name":"Unsupervised segmentation for MR brain images","publication_year":2011,"publication_date":"2011-10-26","ids":{"openalex":"https://openalex.org/W1973761609","doi":"https://doi.org/10.1145/2093698.2093742","mag":"1973761609"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/2093698.2093742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5004284371","display_name":"Kazuhito Sato","orcid":"https://orcid.org/0009-0006-3045-5112"},"institutions":[{"id":"https://openalex.org/I5467274","display_name":"Akita Prefectural University","ror":"https://ror.org/05b1kx621","country_code":"JP","type":"education","lineage":["https://openalex.org/I5467274"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhito Sato","raw_affiliation_strings":["Akita Prefectural University, Tsuchiya-Ebinokuchi, Yuri-Honjo, Japan"],"affiliations":[{"raw_affiliation_string":"Akita Prefectural University, Tsuchiya-Ebinokuchi, Yuri-Honjo, Japan","institution_ids":["https://openalex.org/I5467274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112305759","display_name":"Sakura Kadowaki","orcid":null},"institutions":[],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sakura Kadowaki","raw_affiliation_strings":["Smart Design Corp., Hodono, Suwa-cho, Akita, Japan"],"affiliations":[{"raw_affiliation_string":"Smart Design Corp., Hodono, Suwa-cho, Akita, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015549649","display_name":"Hirokazu Madokoro","orcid":"https://orcid.org/0000-0001-5485-2928"},"institutions":[{"id":"https://openalex.org/I5467274","display_name":"Akita Prefectural University","ror":"https://ror.org/05b1kx621","country_code":"JP","type":"education","lineage":["https://openalex.org/I5467274"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hirokazu Madokoro","raw_affiliation_strings":["Akita Prefectural University, Tsuchiya-Ebinokuchi, Yuri-Honjo, Japan"],"affiliations":[{"raw_affiliation_string":"Akita Prefectural University, Tsuchiya-Ebinokuchi, Yuri-Honjo, Japan","institution_ids":["https://openalex.org/I5467274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101050616","display_name":"Momoyo Ito","orcid":null},"institutions":[{"id":"https://openalex.org/I922474255","display_name":"Tokushima University","ror":"https://ror.org/044vy1d05","country_code":"JP","type":"education","lineage":["https://openalex.org/I922474255"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Momoyo Ito","raw_affiliation_strings":["The University of Tokushima, Minamijyousanjima-cho, Tokushima, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokushima, Minamijyousanjima-cho, Tokushima, Japan","institution_ids":["https://openalex.org/I922474255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020568659","display_name":"Atsushi Inugami","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135494","display_name":"Akita Red Cross Hospital","ror":"https://ror.org/043h2w593","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I2799517759","https://openalex.org/I4210135494"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsushi Inugami","raw_affiliation_strings":["Akita Kumiai, General Hospital, Iijima Nishibukuro, Akita, Japan"],"affiliations":[{"raw_affiliation_string":"Akita Kumiai, General Hospital, Iijima Nishibukuro, Akita, Japan","institution_ids":["https://openalex.org/I4210135494"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.868,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":3,"citation_normalized_percentile":{"value":0.389381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":76,"max":78},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9986,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9986,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9931,"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/T10320","display_name":"Neural Networks and Applications","score":0.9781,"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"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.71130556},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7100457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5897796},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.559224},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5201107},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46854627},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.45335874},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.4239368},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.08638927},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/2093698.2093742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality education","score":0.89}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":13,"referenced_works":["https://openalex.org/W1494231131","https://openalex.org/W1576753221","https://openalex.org/W1625110253","https://openalex.org/W2004001704","https://openalex.org/W2027423884","https://openalex.org/W2103316308","https://openalex.org/W2117695123","https://openalex.org/W2147535728","https://openalex.org/W2151743452","https://openalex.org/W2153110463","https://openalex.org/W2615412239","https://openalex.org/W3023537567","https://openalex.org/W4213332169"],"related_works":["https://openalex.org/W4256129901","https://openalex.org/W3151415490","https://openalex.org/W3021493803","https://openalex.org/W2912860444","https://openalex.org/W2257755506","https://openalex.org/W2011111248","https://openalex.org/W2006468016","https://openalex.org/W2002934375","https://openalex.org/W1907269663","https://openalex.org/W1522196789"],"abstract_inverted_index":{"As":[0,37],"described":[1],"herein,":[2],"we":[3,65,146,160],"propose":[4,161],"an":[5],"unsupervised":[6],"method":[7,40,150],"for":[8,60,167,185],"segmentation":[9],"of":[10,21,31,78,86,100,120,123,133,136,177,193,196,201,204,223,234],"magnetic":[11],"resonance":[12],"(MR)":[13],"brain":[14,61,170,197,205,224],"images":[15],"by":[16],"hybridizing":[17],"the":[18,38,42,67,72,84,87,91,121,134,139,143,194,211,218,221,235],"self-mapping":[19],"characteristics":[20],"1-D":[22],"Self-Organizing":[23],"Maps":[24],"(SOMs)":[25],"and":[26,54,111,125],"using":[27],"incremental":[28],"learning":[29],"functions":[30],"fuzzy":[32],"Adaptive":[33],"Resonance":[34],"Theory":[35],"(ART).":[36],"proposed":[39,88],"requires":[41],"appropriate":[43],"parameters":[44],"to":[45,82,227],"segment":[46],"tissues":[47],"(such":[48],"as":[49,102],"cerebrospinal":[50],"fluid,":[51],"gray":[52],"matter":[53],"white":[55],"matter)":[56],"that":[57,94,148,210],"are":[58,95],"necessary":[59],"atrophy":[62,225],"diagnosis,":[63],"first":[64],"derive":[66],"optimal":[68],"parameter":[69],"set":[70],"through":[71],"preliminary":[73],"experiments.":[74],"The":[75],"main":[76],"contribution":[77],"this":[79],"work":[80,216],"is":[81],"evaluate":[83],"effectiveness":[85],"method,":[89],"considering":[90],"conventional":[92,157],"methods":[93],"highly":[96],"accurate":[97],"in":[98,217],"terms":[99],"usefulness":[101],"classification":[103],"techniques.":[104],"We":[105,180],"focus":[106],"on":[107,142,174,188],"Fuzzy":[108],"C-means":[109],"(FCM)":[110],"Expectation":[112],"Maximization":[113],"Gaussian":[114],"Mixture":[115],"(EM-GM)":[116],"with":[117,169],"previous":[118,131],"setting":[119,132],"number":[122,135],"clusters,":[124],"then":[126],"Mean":[127],"Shift":[128],"(MS)":[129],"without":[130],"clusters.":[137],"Through":[138],"comparative":[140],"experiments":[141],"two":[144],"metrics,":[145],"confirmed":[147],"our":[149],"could":[151],"achieve":[152],"higher":[153],"accuracy":[154],"than":[155],"these":[156],"methods.":[158],"Additionally,":[159],"a":[162,182],"Computer-Aided":[163],"Diagnosis":[164],"(CAD)":[165],"system":[166,184,212],"use":[168],"dock":[171,206],"examinations":[172],"based":[173],"case":[175],"analyses":[176],"diagnostic":[178,215],"reading.":[179],"construct":[181],"prototype":[183],"reducing":[186],"loads":[187],"diagnosticians":[189],"during":[190],"quantitative":[191],"analysis":[192],"degree":[195],"atrophy.":[198],"Field":[199],"tests":[200],"193":[202],"examples":[203],"medical":[207],"examinees":[208],"reveal":[209],"efficiently":[213],"supports":[214],"clinical":[219],"field:":[220],"alteration":[222],"attributable":[226],"aging":[228],"can":[229],"be":[230],"quantified":[231],"easily,":[232],"irrespective":[233],"diagnostician.":[236]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W1973761609","counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-01-19T19:47:43.705149","created_date":"2016-06-24"}