{"id":"https://openalex.org/W2922180175","doi":"https://doi.org/10.1117/12.2506805","title":"Incorporating CT prior information in the robust fuzzy C-means algorithm for QSPECT image segmentation","display_name":"Incorporating CT prior information in the robust fuzzy C-means algorithm for QSPECT image segmentation","publication_year":2019,"publication_date":"2019-03-15","ids":{"openalex":"https://openalex.org/W2922180175","doi":"https://doi.org/10.1117/12.2506805","mag":"2922180175"},"language":"en","primary_location":{"id":"doi:10.1117/12.2506805","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2506805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","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/A5100637983","display_name":"Junyu Chen","orcid":"https://orcid.org/0000-0002-3407-1005"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junyu Chen","raw_affiliation_strings":["Johns Hopkins Univ. (United States)"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins Univ. (United States)","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081456414","display_name":"Abhinav K. Jha","orcid":"https://orcid.org/0000-0002-4795-4878"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav K. Jha","raw_affiliation_strings":["Washington University in St. Louis, United#N#                     States"],"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, United#N#                     States","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075316302","display_name":"Eric C. Frey","orcid":"https://orcid.org/0000-0002-9596-3955"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric C. Frey","raw_affiliation_strings":["Johns Hopkins Univ. (United States)"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins Univ. (United States)","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100637983"],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":1.3547,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.80635351,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"58","issue":null,"first_page":"66","last_page":"66"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9998999834060669,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9991000294685364,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6953471302986145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6628569960594177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6121593117713928},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6094698905944824},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5745429992675781},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4786423444747925},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4403200149536133},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.43051207065582275},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4245016872882843},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4136464595794678},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38412603735923767},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22732043266296387}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6953471302986145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6628569960594177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6121593117713928},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6094698905944824},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5745429992675781},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4786423444747925},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4403200149536133},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.43051207065582275},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4245016872882843},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4136464595794678},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38412603735923767},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22732043266296387}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2506805","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2506805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1988194805","https://openalex.org/W1995450389","https://openalex.org/W2036458679","https://openalex.org/W2053958583","https://openalex.org/W2108859253","https://openalex.org/W2113283292","https://openalex.org/W2116040708","https://openalex.org/W2132513126","https://openalex.org/W2154744699","https://openalex.org/W2162340898","https://openalex.org/W2165012164","https://openalex.org/W2166365825","https://openalex.org/W2590503332"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W1926736923","https://openalex.org/W2158836806","https://openalex.org/W1522196789","https://openalex.org/W4379141755","https://openalex.org/W4367060753"],"abstract_inverted_index":{"Bones":[0],"are":[1],"a":[2,8,90],"common":[3],"site":[4],"of":[5,10,43,73,77,124,162,226,229,250],"metastases":[6],"in":[7,50,113,128,193],"number":[9,228],"cancers":[11],"including":[12],"prostate":[13],"and":[14,48,58,70,88,121,178,191,207],"breast":[15],"cancer.":[16],"Assessing":[17],"response":[18],"or":[19],"progression":[20],"typically":[21],"relies":[22],"on":[23,99],"planar":[24],"bone":[25,29,78,174,192],"scintigraphy.":[26],"However,":[27,62],"quantitative":[28,100,103],"SPECT":[30,74,79,125,183],"(BQSPECT)":[31],"has":[32,109],"the":[33,65,118,151,159,163,182,215,227,234,241,261],"potential":[34],"to":[35,52,64,133,144,169,185,214,232],"provide":[36],"more":[37],"accurate":[38],"assessment.":[39],"An":[40],"important":[41],"component":[42],"BQSPECT":[44,203,235,243],"is":[45,81],"segmenting":[46,189],"lesions":[47,255],"bones":[49,147],"order":[51],"calculate":[53],"metrics":[54],"like":[55],"tumor":[56,60],"uptake":[57],"metabolic":[59],"burden.":[61],"due":[63],"poor":[66,119],"spatial":[67,179],"resolution,":[68],"noise,":[69],"contrast":[71],"properties":[72,123],"images,":[75,139],"segmentation":[76,97,153,222],"images":[80,126,177,236,244],"challenging.":[82],"In":[83],"this":[84],"study,":[85],"we":[86],"propose":[87,132],"evaluate":[89],"fuzzy":[91,165],"C-means":[92,166],"(FCM)":[93],"clustering":[94,107],"based":[95],"semi-automatic":[96],"method":[98,157,168,197,206],"Tc-99m":[101],"MDP":[102],"SPECT/CT.":[104],"The":[105,155,196,205,224],"FCM":[106,152],"algorithm":[108,208],"been":[110],"widely":[111],"used":[112,143,231],"medical":[114],"image":[115,184],"segmentation.":[116,130],"Yet,":[117],"resolution":[120],"noise":[122],"result":[127],"sub-optimal":[129],"We":[131],"incorporate":[134],"information":[135,172,180],"from":[136,175,181],"registered":[137],"CT":[138,176],"which":[140],"can":[141],"be":[142],"segment":[145],"normal":[146],"quite":[148],"readily,":[149],"into":[150],"algorithm.":[154],"proposed":[156,262],"modifies":[158],"objective":[160],"function":[161],"robust":[164],"(RFCM)":[167],"include":[170],"prior":[171],"about":[173],"allow":[186],"for":[187,254],"simultaneously":[188],"lesion":[190],"BQSPECT/CT":[194],"images.":[195,204],"was":[198,237,252],"evaluated":[199,211],"using":[200,221],"realistic":[201],"simulated":[202,242],"parameters":[209],"were":[210],"with":[212,260],"respect":[213],"dice":[216],"similarity":[217],"coefficient":[218],"(DSC)":[219],"computed":[220],"results.":[223],"effect":[225],"iterations":[230],"reconstruct":[233],"also":[238],"studied.":[239],"For":[240],"studied,":[245],"an":[246],"average":[247],"DSC":[248],"value":[249],"0.75":[251],"obtained":[253],"larger":[256],"than":[257],"2":[258],"cm<sup>3</sup>":[259],"method.":[263]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}