{"id":"https://openalex.org/W2084089460","doi":"https://doi.org/10.1117/12.535542","title":"Reliability measure for segmenting algorithms","display_name":"Reliability measure for segmenting algorithms","publication_year":2004,"publication_date":"2004-05-04","ids":{"openalex":"https://openalex.org/W2084089460","doi":"https://doi.org/10.1117/12.535542","mag":"2084089460"},"language":"en","primary_location":{"id":"doi:10.1117/12.535542","doi":"https://doi.org/10.1117/12.535542","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.535542","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5109962844","display_name":"Robert E. Alvarez","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Robert E. Alvarez","raw_affiliation_strings":["Aprend Technology (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aprend Technology (United States)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5109962844"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15890589,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5372","issue":null,"first_page":"521","last_page":"521"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9976000189781189,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9976000189781189,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.0003000000142492354,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.00019999999494757503,"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/computer-aided-detection","display_name":"computer-aided detection","score":0.9089999794960022},{"id":"https://openalex.org/keywords/statistical-shape-models","display_name":"statistical shape models","score":0.9190000295639038},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.949999988079071},{"id":"https://openalex.org/keywords/residual-analysis","display_name":"residual analysis","score":0.7540000081062317},{"id":"https://openalex.org/keywords/gaussian-transformation","display_name":"Gaussian transformation","score":0.5529999732971191},{"id":"https://openalex.org/keywords/chest-x-ray","display_name":"chest radiography","score":0.5400000214576721},{"id":"https://openalex.org/keywords/lung-segmentation","display_name":"lung segmentation","score":0.7549999952316284},{"id":"https://openalex.org/keywords/automatic-segmentation","display_name":"automatic segmentation","score":0.32199999690055847}],"concepts":[{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.7545198798179626},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.7102737426757812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6344070434570312},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4850255846977234},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.47315487265586853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3455714285373688},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2623227834701538},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.535542","doi":"https://doi.org/10.1117/12.535542","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.535542","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2156025712","https://openalex.org/W2012080616","https://openalex.org/W2135175338","https://openalex.org/W2105151778","https://openalex.org/W2611247216","https://openalex.org/W2523307289","https://openalex.org/W2912342811","https://openalex.org/W2137564964","https://openalex.org/W7095079714","https://openalex.org/W2026077275"],"abstract_inverted_index":{"Segmenting":[0],"is":[1,14,80,101],"a":[2,17,29,41,50,83,146],"key":[3],"initial":[4],"step":[5],"in":[6,162,185,197],"many":[7],"computer-aided":[8],"detection":[9],"(CAD)":[10],"systems.":[11],"Our":[12],"purpose":[13],"to":[15,19,82,180,230],"develop":[16],"method":[18],"estimate":[20],"the":[21,54,59,66,76,94,102,105,114,118,123,153,157,160,163,176,182,198,215,223,235],"reliability":[22,99,124,183,189,225],"of":[23,44,53,68,75,78,117,156,159],"segmenting":[24,60],"algorithm":[25],"results.":[26],"We":[27,91,121],"use":[28,93],"statistical":[30,147],"shape":[31,148],"model":[32,39,149],"computed":[33,151],"using":[34,139],"principal":[35],"component":[36],"analysis.":[37],"The":[38,56,73,98,134,166,188,208],"retains":[40],"small":[42],"number":[43],"eigenvectors,":[45],"or":[46,205,233],"modes,":[47],"that":[48,104],"represent":[49],"large":[51],"fraction":[52],"variance.":[55],"residuals":[57,79,107,167],"between":[58],"result":[61],"and":[62,108,168,210],"its":[63],"projection":[64],"into":[65],"space":[67],"retained":[69],"modes":[70],"are":[71,111,217],"computed.":[72],"sum":[74],"squares":[77],"transformed":[81,106],"zero-mean,":[84],"unit":[85],"standard":[86],"deviation":[87],"Gaussian":[88,135],"random":[89],"variable.":[90],"also":[92],"standardized":[95],"scale":[96,109,169],"parameter.":[97],"measure":[100,125,184,190,226],"probability":[103,141],"parameter":[110,170],"greater":[112],"than":[113],"absolute":[115],"value":[116],"observed":[119],"values.":[120],"tested":[122],"with":[126,131,171,201],"thirty":[127],"chest":[128],"x-ray":[129],"images":[130,161,196],"\u201cleave-out-one\u201d":[132],"testing.":[133],"assumption":[136],"was":[137,150,191],"verified":[138],"normal":[140],"plots.":[142],"For":[143],"each":[144,186],"image,":[145],"from":[152],"hand-digitized":[154],"data":[155,209],"rest":[158],"training":[164,199],"set.":[165],"automated":[172],"segment":[173],"results":[174],"for":[175,194,213],"image":[177],"were":[178],"used":[179,229],"compute":[181],"case.":[187],"significantly":[192],"lower":[193],"two":[195],"set":[200],"unusual":[202],"lung":[203],"fields":[204],"processing":[206,232],"errors.":[207],"Matlab":[211],"scripts":[212],"reproducing":[214],"figures":[216],"at":[218],"http://www.aprendtech.com/papers/relmsr.zip":[219],"Errors":[220],"detected":[221],"by":[222],"new":[224],"can":[227],"be":[228],"adjust":[231],"warn":[234],"user.":[236]},"counts_by_year":[],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2016-06-24T00:00:00"}
