{"id":"https://openalex.org/W2921550758","doi":"https://doi.org/10.1117/12.2511718","title":"Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images","display_name":"Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images","publication_year":2019,"publication_date":"2019-03-13","ids":{"openalex":"https://openalex.org/W2921550758","doi":"https://doi.org/10.1117/12.2511718","mag":"2921550758"},"language":"en","primary_location":{"id":"doi:10.1117/12.2511718","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2511718","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","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/A5018325225","display_name":"Marco Caballo","orcid":"https://orcid.org/0000-0002-3700-2785"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Marco Caballo","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031023373","display_name":"Jonas Teuwen","orcid":"https://orcid.org/0000-0002-1825-1428"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Jonas Teuwen","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073381909","display_name":"Ritse M. Mann","orcid":"https://orcid.org/0000-0001-8111-1930"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ritse Mann","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079270012","display_name":"Ioannis Sechopoulos","orcid":"https://orcid.org/0000-0001-9615-8205"},"institutions":[{"id":"https://openalex.org/I4210113394","display_name":"Dutch Expert Centre for Screening","ror":"https://ror.org/02braec51","country_code":"NL","type":"other","lineage":["https://openalex.org/I4210113394"]},{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ioannis Sechopoulos","raw_affiliation_strings":["Dutch Expert Ctr. for Screening (Netherlands)","Radboud Univ. Medical Ctr. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Dutch Expert Ctr. for Screening (Netherlands)","institution_ids":["https://openalex.org/I4210113394"]},{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5018325225"],"corresponding_institution_ids":["https://openalex.org/I145872427"],"apc_list":null,"apc_paid":null,"fwci":0.7001,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77063875,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"59","issue":null,"first_page":"18","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9998000264167786,"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7828680872917175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7677139639854431},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5828092098236084},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5696782469749451},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5470295548439026},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.5448321104049683},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4486888349056244},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.44274285435676575},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.44045963883399963},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.433277428150177},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.42665213346481323}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7828680872917175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7677139639854431},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5828092098236084},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5696782469749451},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5470295548439026},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5448321104049683},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4486888349056244},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.44274285435676575},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.44045963883399963},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.433277428150177},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.42665213346481323},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"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:10.1117/12.2511718","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2511718","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W783096245","https://openalex.org/W1804689138","https://openalex.org/W1996591481","https://openalex.org/W2003304826","https://openalex.org/W2013504223","https://openalex.org/W2026707626","https://openalex.org/W2039612688","https://openalex.org/W2040823105","https://openalex.org/W2044465660","https://openalex.org/W2051812123","https://openalex.org/W2065698093","https://openalex.org/W2070143562","https://openalex.org/W2107167693","https://openalex.org/W2123683167","https://openalex.org/W2126466327","https://openalex.org/W2148141447","https://openalex.org/W2149152304","https://openalex.org/W2163352848","https://openalex.org/W2174661749","https://openalex.org/W2394987826","https://openalex.org/W2549975402","https://openalex.org/W2755087487","https://openalex.org/W2963785858","https://openalex.org/W3016960874","https://openalex.org/W4239524281","https://openalex.org/W4246711887","https://openalex.org/W6627672905","https://openalex.org/W6660288226","https://openalex.org/W6662643487","https://openalex.org/W6729089581","https://openalex.org/W6776402278"],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W183202219","https://openalex.org/W3095877357","https://openalex.org/W2790258528","https://openalex.org/W2095030957","https://openalex.org/W2066827917"],"abstract_inverted_index":{"We":[0,59,106],"propose":[1],"an":[2,97,186],"algorithm":[3],"to":[4,120,157],"recognize":[5],"breast":[6,14,24,56,191,247],"parenchyma":[7,36],"regions":[8],"containing":[9,30,54,138,198],"mass-like":[10],"abnormalities":[11],"in":[12,96,161,221,246],"dedicated":[13],"CT":[15,25,192,248],"images":[16],"using":[17,185],"texture":[18,62],"feature":[19],"descriptors.":[20],"From":[21],"53":[22],"patient":[23,190],"scans":[26,193],"(29":[27],"of":[28,45,88,104,142,204],"which":[29,81,212],"masses),":[31],"we":[32,41,82],"first":[33],"isolated":[34],"the":[35,100,109,129,139,153,158,164,175,217,233],"through":[37,85,113],"automatic":[38],"segmentation,":[39],"and":[40,52,75,91,117,122,145,196,226,240],"obtained":[42],"a":[43,55,124,199,202],"total":[44,203],"14,751":[46],"normal":[47,195],"2D":[48],"image":[49,206],"patches":[50,207],"(negatives),":[51],"2,100":[53],"mass":[57],"(positives).":[58],"extracted":[60],"141":[61],"features":[63,112],"(10":[64],"first-order":[65],"descriptors,":[66,77],"6":[67],"Haralick":[68],"features,":[69,72],"20":[70],"run-length":[71],"45":[73],"structural":[74],"pattern":[76],"60":[78],"Gabor":[79],"features),":[80],"then":[83],"analyzed":[84],"multivariate":[86],"analysis":[87],"variance":[89],"(MANOVA)":[90],"linear":[92],"discriminant":[93,111],"analysis,":[94],"resulting":[95,220],"area":[98],"under":[99],"ROC":[101],"curve":[102],"(AUC)":[103],"0.9.":[105],"finally":[107],"identified":[108],"most":[110],"sequential":[114],"forward":[115],"selection,":[116],"used":[118,215],"them":[119],"train":[121],"validate":[123],"neural":[125],"network":[126,176,182],"by":[127],"dividing":[128],"data":[130],"into":[131],"multiple":[132],"batches,":[133],"with":[134],"each":[135,179],"batch":[136],"always":[137],"whole":[140],"set":[141],"positive":[143],"cases,":[144],"as":[146],"many":[147],"different":[148],"negative":[149],"examples.":[150],"To":[151],"avoid":[152],"possible":[154],"bias":[155],"due":[156],"high":[159],"skewness":[160],"class":[162],"proportion,":[163],"training":[165],"was":[166,183],"performed":[167],"on":[168,201],"all":[169],"these":[170],"batches":[171],"independently,":[172],"without":[173],"re-initializing":[174],"weights":[177],"after":[178],"training.":[180],"The":[181],"tested":[184],"additional":[187],"independent":[188],"18":[189],"(8":[194],"10":[197],"mass),":[200],"7,274":[205],"(852":[208],"positives,":[209],"6,422":[210],"negatives)":[211],"were":[213],"not":[214],"during":[216],"training/validation":[218],"phase,":[219],"95.6%":[222],"precision,":[223],"95.8%":[224],"recall,":[225],"0.99":[227],"AUC.":[228],"Our":[229],"results":[230],"suggest":[231],"that":[232],"proposed":[234],"approach":[235],"could":[236],"be":[237],"further":[238],"evaluated":[239],"expanded":[241],"for":[242],"computer-aided":[243],"detection":[244],"tasks":[245],"imaging.":[249]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
