{"id":"https://openalex.org/W2114642404","doi":"https://doi.org/10.1109/isbi.2011.5872496","title":"Segmentation of anatomical branching structures based on texture features and graph cut","display_name":"Segmentation of anatomical branching structures based on texture features and graph cut","publication_year":2011,"publication_date":"2011-03-01","ids":{"openalex":"https://openalex.org/W2114642404","doi":"https://doi.org/10.1109/isbi.2011.5872496","mag":"2114642404"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2011.5872496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2011.5872496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","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/A5002240541","display_name":"Tatyana Nuzhnaya","orcid":null},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tatyana Nuzhnaya","raw_affiliation_strings":["Data Engineering Laboratory (DEnLab), Temple University, Philadelphia, PA, USA","Data Engineering Laboratory (DEnLab), Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA"],"affiliations":[{"raw_affiliation_string":"Data Engineering Laboratory (DEnLab), Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]},{"raw_affiliation_string":"Data Engineering Laboratory (DEnLab), Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059137735","display_name":"Erkang Cheng","orcid":"https://orcid.org/0000-0001-7941-6911"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erkang Cheng","raw_affiliation_strings":["Data Engineering Laboratory (DEnLab), Center for Information Science and Technology, Philadelphia, PA, USA","Center for Information Science and Technology, Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA"],"affiliations":[{"raw_affiliation_string":"Data Engineering Laboratory (DEnLab), Center for Information Science and Technology, Philadelphia, PA, USA","institution_ids":[]},{"raw_affiliation_string":"Center for Information Science and Technology, Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061469520","display_name":"Haibin Ling","orcid":"https://orcid.org/0000-0003-4094-8413"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haibin Ling","raw_affiliation_strings":["Data Engineering Laboratory (DEnLab), Center for Information Science and Technology, Philadelphia, PA, USA","Center for Information Science and Technology, Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA"],"affiliations":[{"raw_affiliation_string":"Data Engineering Laboratory (DEnLab), Center for Information Science and Technology, Philadelphia, PA, USA","institution_ids":[]},{"raw_affiliation_string":"Center for Information Science and Technology, Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060899856","display_name":"Despina Kontos","orcid":"https://orcid.org/0000-0001-9031-5126"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Despina Kontos","raw_affiliation_strings":["Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA","Department of Radiology, University of Pennsylvania, 3400 Spruce St., Philadelphia, 19104, USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Department of Radiology, University of Pennsylvania, 3400 Spruce St., Philadelphia, 19104, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054510591","display_name":"Predrag R. Baki\u0107","orcid":"https://orcid.org/0000-0001-7087-0915"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Predrag R. Bakic","raw_affiliation_strings":["Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA","Department of Radiology, University of Pennsylvania, 3400 Spruce St., Philadelphia, 19104, USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Department of Radiology, University of Pennsylvania, 3400 Spruce St., Philadelphia, 19104, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046892627","display_name":"Vasileios Megalooikonomou","orcid":null},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vasileios Megalooikonomou","raw_affiliation_strings":["Data Engineering Laboratory (DEnLab), Temple University, Philadelphia, PA, USA","Data Engineering Laboratory (DEnLab), Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA"],"affiliations":[{"raw_affiliation_string":"Data Engineering Laboratory (DEnLab), Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]},{"raw_affiliation_string":"Data Engineering Laboratory (DEnLab), Temple University, 1805 N. Broad St. Philadelphia, PA 19122, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5002240541"],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":null,"apc_paid":null,"fwci":0.4386,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74111713,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"673","last_page":"676"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9991000294685364,"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":0.9991000294685364,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9829000234603882,"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":0.970300018787384,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.757177472114563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7401512861251831},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6481262445449829},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6127790808677673},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6041658520698547},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5767161250114441},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.55987948179245},{"id":"https://openalex.org/keywords/skewness","display_name":"Skewness","score":0.5275095105171204},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43293601274490356},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.42639949917793274},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2544329762458801},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0849776566028595},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.08353990316390991}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.757177472114563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7401512861251831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6481262445449829},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6127790808677673},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6041658520698547},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5767161250114441},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.55987948179245},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.5275095105171204},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43293601274490356},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.42639949917793274},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2544329762458801},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0849776566028595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08353990316390991}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isbi.2011.5872496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2011.5872496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.204.2268","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.204.2268","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ist.temple.edu/%7Ehbling/publication/vessel_seg_isbi11.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W67868186","https://openalex.org/W1490342204","https://openalex.org/W1987983010","https://openalex.org/W2015011762","https://openalex.org/W2039336606","https://openalex.org/W2044465660","https://openalex.org/W2060762784","https://openalex.org/W2101912845","https://openalex.org/W2104095591","https://openalex.org/W2112186137","https://openalex.org/W2120666298","https://openalex.org/W2140432463","https://openalex.org/W2169551590","https://openalex.org/W6660607668","https://openalex.org/W6675770446"],"related_works":["https://openalex.org/W2189529952","https://openalex.org/W3122032734","https://openalex.org/W28389960","https://openalex.org/W2375137989","https://openalex.org/W2010171670","https://openalex.org/W2109407305","https://openalex.org/W2032319136","https://openalex.org/W2897997384","https://openalex.org/W1544828638","https://openalex.org/W2000036183"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,38,49,68,90,108,127,163,170,176],"tree-like":[2],"structure":[3,173],"within":[4],"medical":[5],"imaging":[6],"modalities,":[7],"such":[8],"as":[9],"x-ray,":[10],"MRI,":[11],"ultrasound,":[12],"etc.,":[13],"is":[14,77],"an":[15],"important":[16],"step":[17],"for":[18,65],"analyzing":[19],"branching":[20,178],"patterns":[21],"involved":[22],"in":[23,72,160],"many":[24],"anatomic":[25],"structures.":[26],"However,":[27],"images":[28,165],"acquired":[29],"using":[30],"these":[31],"different":[32],"acquisition":[33],"techniques":[34],"frequently":[35],"have":[36],"features":[37,89,99],"poor":[39],"contrast,":[40,93],"blurring":[41],"and":[42,44,84,95,166,174],"noise,":[43],"therefore":[45],"the":[46,69,80,109,141,146,161,168,177],"segmentation":[47,52,67],"result":[48],"traditional":[50],"image":[51],"methods":[53],"may":[54,156],"not":[55,104],"be":[56],"satisfactory.":[57],"In":[58],"this":[59],"paper,":[60],"we":[61],"propose":[62],"a":[63,125],"framework":[64],"accurate":[66],"ductal":[70,111],"network":[71],"x-ray":[73],"galactograms.":[74],"Our":[75],"approach":[76,121,155],"based":[78],"on":[79],"graph":[81],"cut":[82],"algorithm":[83],"texture":[85,175],"analysis":[86,138],"to":[87,102,124,139],"extract":[88],"skewness,":[91],"coarseness,":[92],"energy":[94],"fractal":[96],"dimension.":[97],"The":[98,119,143],"are":[100],"chosen":[101],"capture":[103],"only":[105],"architectural":[106],"variability":[107],"enhanced":[110],"tree,":[112],"but":[113],"also":[114],"spatial":[115],"variations":[116],"among":[117,172],"pixels.":[118],"proposed":[120],"was":[122,150],"applied":[123],"dataset":[126],"20":[128],"galactographic":[129],"images.":[130],"We":[131],"performed":[132],"receiver":[133],"operating":[134],"characteristic":[135],"(ROC)":[136],"curve":[137,148],"assess":[140],"accuracy.":[142],"area":[144],"under":[145],"ROC":[147],"observed":[149],"0.76,":[151],"indicating":[152],"that":[153],"our":[154],"potentially":[157],"assist":[158],"clinicians":[159],"interpretation":[162],"breast":[164],"facilitate":[167],"investigation":[169],"relationships":[171],"patterns.":[179]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
