{"id":"https://openalex.org/W2838806391","doi":"https://doi.org/10.1117/12.2317589","title":"Superpixel pattern graphs for identifying breast mass ROIs in dense background: a preliminary study","display_name":"Superpixel pattern graphs for identifying breast mass ROIs in dense background: a preliminary study","publication_year":2018,"publication_date":"2018-07-06","ids":{"openalex":"https://openalex.org/W2838806391","doi":"https://doi.org/10.1117/12.2317589","mag":"2838806391"},"language":"en","primary_location":{"id":"doi:10.1117/12.2317589","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2317589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"14th International Workshop on Breast Imaging (IWBI 2018)","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/A5034881808","display_name":"Shelda Sajeev","orcid":"https://orcid.org/0000-0002-7428-4435"},"institutions":[{"id":"https://openalex.org/I169541294","display_name":"Flinders University","ror":"https://ror.org/01kpzv902","country_code":"AU","type":"education","lineage":["https://openalex.org/I169541294"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shelda Sajeev","raw_affiliation_strings":["Flinders Univ. (Australia)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Flinders Univ. (Australia)","institution_ids":["https://openalex.org/I169541294"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075061698","display_name":"Mariusz Bajger","orcid":"https://orcid.org/0000-0003-1866-2451"},"institutions":[{"id":"https://openalex.org/I169541294","display_name":"Flinders University","ror":"https://ror.org/01kpzv902","country_code":"AU","type":"education","lineage":["https://openalex.org/I169541294"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mariusz Bajger","raw_affiliation_strings":["Flinders Univ. (Australia)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Flinders Univ. (Australia)","institution_ids":["https://openalex.org/I169541294"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048728333","display_name":"Gobert Lee","orcid":"https://orcid.org/0000-0001-8330-0508"},"institutions":[{"id":"https://openalex.org/I169541294","display_name":"Flinders University","ror":"https://ror.org/01kpzv902","country_code":"AU","type":"education","lineage":["https://openalex.org/I169541294"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Gobert Lee","raw_affiliation_strings":["Flinders Univ. (Australia)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Flinders Univ. (Australia)","institution_ids":["https://openalex.org/I169541294"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.169,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59503388,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"53","issue":null,"first_page":"9","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9904999732971191,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7409473657608032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6628130674362183},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6124871373176575},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.5813641548156738},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5245371460914612},{"id":"https://openalex.org/keywords/breast-tissue","display_name":"Breast tissue","score":0.520940899848938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.476018488407135},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4717096984386444},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43065398931503296},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43033134937286377},{"id":"https://openalex.org/keywords/breast-density","display_name":"Breast density","score":0.42091524600982666},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3251394033432007},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.3127255439758301},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.28051185607910156},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.07436713576316833},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.07264187932014465}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7409473657608032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6628130674362183},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6124871373176575},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.5813641548156738},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5245371460914612},{"id":"https://openalex.org/C3020109028","wikidata":"https://www.wikidata.org/wiki/Q9103","display_name":"Breast tissue","level":4,"score":0.520940899848938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.476018488407135},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4717096984386444},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43065398931503296},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43033134937286377},{"id":"https://openalex.org/C3018951153","wikidata":"https://www.wikidata.org/wiki/Q17011492","display_name":"Breast density","level":5,"score":0.42091524600982666},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3251394033432007},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.3127255439758301},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.28051185607910156},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.07436713576316833},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.07264187932014465},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2317589","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2317589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"14th International Workshop on Breast Imaging (IWBI 2018)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W177004468","https://openalex.org/W1075124913","https://openalex.org/W1974001716","https://openalex.org/W1977579905","https://openalex.org/W2032533296","https://openalex.org/W2066387791","https://openalex.org/W2076360002","https://openalex.org/W2152034657","https://openalex.org/W2152632881","https://openalex.org/W2163352848","https://openalex.org/W2493683088","https://openalex.org/W2558523656","https://openalex.org/W2777240482","https://openalex.org/W6607184829","https://openalex.org/W6730199148","https://openalex.org/W6747156306"],"related_works":["https://openalex.org/W1991255418","https://openalex.org/W2320814173","https://openalex.org/W2138691429","https://openalex.org/W2997597260","https://openalex.org/W2121126942","https://openalex.org/W2344745381","https://openalex.org/W183642269","https://openalex.org/W96589957","https://openalex.org/W2727051654","https://openalex.org/W4402978774"],"abstract_inverted_index":{"Finding":[0],"mamographic":[1],"masses":[2,27,43,136,169],"located":[3],"in":[4,45,60,126,137,171],"a":[5,10,37],"dense":[6,32,46,108,127,138,144,172],"breast":[7,49,145,168],"tissue":[8],"is":[9,50],"challenge":[11],"even":[12],"for":[13,40,98,117,187],"an":[14],"experienced":[15],"radiologist.":[16],"The":[17,52,65,100,147],"difficulty":[18],"comes":[19],"from":[20,92,111,155],"the":[21,26,29,93,112,151,188],"similarity":[22],"of":[23,42,48,67,84,129,167,183],"intensity":[24],"between":[25,79],"and":[28,81,96,140,163],"overlapped":[30],"normal":[31],"tissues.":[33,146],"In":[34],"this":[35],"study,":[36],"novel":[38],"method":[39,53],"classification":[41],"localized":[44,125,170],"background":[47],"proposed.":[51],"can":[54,159],"identify":[55],"meaningful":[56],"superpixel":[57,68,94,156],"patterns":[58],"present":[59],"mammograms":[61],"within":[62],"mass-like":[63],"regions.":[64],"topology":[66],"patterns,":[69],"captured":[70],"by":[71],"using":[72,105],"spatial":[73],"connectivity":[74],"graphs,":[75],"revealed":[76],"significant":[77],"differences":[78],"cancerous":[80],"healthy":[82,143],"areas":[83],"breasts.":[85,130],"Four":[86],"clinically":[87],"recognizable":[88],"features":[89,153],"were":[90,124],"extracted":[91],"graphs":[95,158],"used":[97],"classification.":[99],"proposed":[101],"approach":[102],"was":[103,185],"evaluated":[104],"ninety":[106],"three":[107],"ROIs":[109,123],"selected":[110],"publicly":[113],"available":[114],"Digital":[115],"Database":[116],"Screening":[118],"Mammography":[119],"(DDSM).":[120],"All":[121],"93":[122],"backgrounds":[128,139],"Among":[131],"them,":[132],"41":[133],"contained":[134,142],"malignant":[135],"52":[141],"results":[148],"indicate":[149],"that":[150],"graph":[152],"generated":[154],"pattern":[157],"produce":[160],"very":[161],"effective":[162],"efficient":[164],"feature":[165,191],"descriptors":[166],"background.":[173],"Using":[174],"Fisher":[175],"Linear":[176],"Discriminant":[177],"Analysis":[178],"(LDA)":[179],"classifier":[180],"AUC":[181],"score":[182],"0.90":[184],"achieved":[186],"four":[189],"dimensional":[190],"vector.":[192]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
