{"id":"https://openalex.org/W1973207530","doi":"https://doi.org/10.1109/prni.2014.6858544","title":"PET imaging analysis using a parcelation approach and multiple kernel classification","display_name":"PET imaging analysis using a parcelation approach and multiple kernel classification","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W1973207530","doi":"https://doi.org/10.1109/prni.2014.6858544","mag":"1973207530"},"language":"en","primary_location":{"id":"doi:10.1109/prni.2014.6858544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2014.6858544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Workshop on Pattern Recognition in Neuroimaging","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/A5018314596","display_name":"F. Segovia","orcid":"https://orcid.org/0000-0003-1940-8834"},"institutions":[{"id":"https://openalex.org/I157674565","display_name":"University of Li\u00e8ge","ror":"https://ror.org/00afp2z80","country_code":"BE","type":"education","lineage":["https://openalex.org/I157674565"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"F. Segovia","raw_affiliation_strings":["Universite de Liege, Liege, BE","Cyclotron Research Centre, University of Li\u00e8ge; Li\u00e8ge Belgium"],"affiliations":[{"raw_affiliation_string":"Universite de Liege, Liege, BE","institution_ids":["https://openalex.org/I157674565"]},{"raw_affiliation_string":"Cyclotron Research Centre, University of Li\u00e8ge; Li\u00e8ge Belgium","institution_ids":["https://openalex.org/I157674565"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005472608","display_name":"Christophe Phillips","orcid":"https://orcid.org/0000-0002-4990-425X"},"institutions":[{"id":"https://openalex.org/I157674565","display_name":"University of Li\u00e8ge","ror":"https://ror.org/00afp2z80","country_code":"BE","type":"education","lineage":["https://openalex.org/I157674565"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"C. Phillips","raw_affiliation_strings":["Universite de Liege, Liege, BE","Cyclotron Research Centre, University of Li\u00e8ge; Li\u00e8ge Belgium"],"affiliations":[{"raw_affiliation_string":"Universite de Liege, Liege, BE","institution_ids":["https://openalex.org/I157674565"]},{"raw_affiliation_string":"Cyclotron Research Centre, University of Li\u00e8ge; Li\u00e8ge Belgium","institution_ids":["https://openalex.org/I157674565"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5018314596"],"corresponding_institution_ids":["https://openalex.org/I157674565"],"apc_list":null,"apc_paid":null,"fwci":0.3837,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68763636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9943000078201294,"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":0.9943000078201294,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9909999966621399,"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/support-vector-machine","display_name":"Support vector machine","score":0.7434693574905396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7259623408317566},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6977529525756836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6360491514205933},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.626718282699585},{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.5999768972396851},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.572967529296875},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5627381205558777},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5284490585327148},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.503694474697113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3614957332611084},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.17082089185714722},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1320648491382599},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12548068165779114}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7434693574905396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7259623408317566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6977529525756836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6360491514205933},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.626718282699585},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.5999768972396851},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.572967529296875},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5627381205558777},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5284490585327148},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.503694474697113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3614957332611084},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.17082089185714722},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1320648491382599},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12548068165779114},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/prni.2014.6858544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2014.6858544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Workshop on Pattern Recognition in Neuroimaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1528119265","https://openalex.org/W1537967768","https://openalex.org/W1575374100","https://openalex.org/W1963984891","https://openalex.org/W1971035997","https://openalex.org/W1977157320","https://openalex.org/W1987011701","https://openalex.org/W1994487817","https://openalex.org/W2017831328","https://openalex.org/W2036035235","https://openalex.org/W2044151123","https://openalex.org/W2058046532","https://openalex.org/W2059114280","https://openalex.org/W2074925665","https://openalex.org/W2076622031","https://openalex.org/W2109743529","https://openalex.org/W2162361716","https://openalex.org/W2171831801","https://openalex.org/W4230674625","https://openalex.org/W6634372553","https://openalex.org/W6676602786"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2981954115"],"abstract_inverted_index":{"Positron":[0],"Emission":[1],"Tomography":[2],"(PET)":[3],"is":[4,26,82,131,162],"a":[5,84,91,134,141,156,182],"noninvasive":[6],"medical":[7],"imaging":[8,81],"modality":[9],"that":[10,98,165,185],"provides":[11],"information":[12,77],"about":[13],"physiological":[14],"processes.":[15],"Due":[16],"to":[17,20,29,45,50,60,94],"its":[18],"ability":[19],"measure":[21],"the":[22,31,47,51,55,67,73,100,109,125,146,166,174,199],"brain":[23],"metabolism,":[24],"it":[25],"widely":[27],"used":[28],"assist":[30],"diagnosis":[32],"of":[33,41,54,76,127],"neurodegenerative":[34],"disorders":[35],"such":[36],"as":[37],"Alzheimer's":[38],"disease":[39],"(AD)":[40],"Parkinsonism.":[42],"In":[43,86],"order":[44],"avoid":[46],"subjectivity":[48],"inherent":[49],"visual":[52],"exploration":[53],"images,":[56],"several":[57],"computer":[58,206],"systems":[59,207],"analyze":[61,95],"PET":[62,80,96,183],"data":[63,97],"were":[64],"developed":[65],"during":[66],"last":[68],"years.":[69],"However,":[70],"dealing":[71],"with":[72,155,198],"huge":[74],"amount":[75],"provided":[78],"by":[79],"still":[83],"challenge.":[85],"this":[87],"work":[88],"we":[89],"present":[90],"novel":[92],"methodology":[93],"improves":[99],"automatic":[101],"differentiation":[102],"between":[103],"controls":[104,190],"and":[105,140,191,213],"AD":[106,192],"patients.":[107,193],"First":[108],"images":[110,187],"are":[111,148],"divided":[112],"into":[113],"small":[114],"regions":[115,147,169],"or":[116,122],"parcels,":[117],"defined":[118],"either":[119],"anatomically,":[120],"geometrically":[121],"randomly.":[123],"Secondly,":[124],"accuray":[126],"each":[128],"single":[129],"region":[130],"estimated":[132],"using":[133,151,181],"Support":[135],"Vector":[136],"Machine":[137],"(SVM)":[138],"classifier":[139,161],"cross-validation":[142],"approach.":[143],"Finally,":[144],"all":[145],"assessed":[149],"together":[150],"multiple":[152],"kernel":[153,157],"SVM":[154],"per":[158],"region.":[159],"The":[160,194],"built":[163],"so":[164],"most":[167],"discriminative":[168],"have":[170],"more":[171],"weight":[172],"in":[173],"final":[175],"decision.":[176],"This":[177],"method":[178],"was":[179],"evaluated":[180],"dataset":[184],"contained":[186],"from":[188],"healthy":[189],"classification":[195],"results":[196],"obtained":[197],"proposed":[200],"approach":[201],"outperformed":[202],"two":[203],"recently":[204],"reported":[205],"based":[208],"on":[209],"Principal":[210],"Component":[211,215],"Analysis":[212],"Independent":[214],"Analysis.":[216]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}