{"id":"https://openalex.org/W2029832839","doi":"https://doi.org/10.1117/12.2081545","title":"Directional denoising and line enhancement for device segmentation in real time fluoroscopic imaging","display_name":"Directional denoising and line enhancement for device segmentation in real time fluoroscopic imaging","publication_year":2015,"publication_date":"2015-03-20","ids":{"openalex":"https://openalex.org/W2029832839","doi":"https://doi.org/10.1117/12.2081545","mag":"2029832839"},"language":"en","primary_location":{"id":"doi:10.1117/12.2081545","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2081545","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,"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"},"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/A5029188793","display_name":"Martin G. Wagner","orcid":"https://orcid.org/0000-0002-7595-797X"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Martin Wagner","raw_affiliation_strings":["University of Wisconsin - Madison, United States;"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin - Madison, United States;","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049885585","display_name":"Kevin Royalty","orcid":"https://orcid.org/0000-0002-5035-7193"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]},{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Royalty","raw_affiliation_strings":["Univ. of Wisconsin-Madison (United States)","Siemens Medical Solutions USA, Inc. (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Wisconsin-Madison (United States)","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"Siemens Medical Solutions USA, Inc. (United States)","institution_ids":["https://openalex.org/I4210137693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039188988","display_name":"Erick L. Oberstar","orcid":"https://orcid.org/0000-0003-0049-2298"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erick Oberstar","raw_affiliation_strings":["University of Wisconsin - Madison, United States;"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin - Madison, United States;","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055137353","display_name":"Charles M. Strother","orcid":"https://orcid.org/0000-0002-4235-8081"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Strother","raw_affiliation_strings":["University of Wisconsin - Madison, United States;"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin - Madison, United States;","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071703198","display_name":"Charles A. Mistretta","orcid":"https://orcid.org/0000-0002-6620-2831"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Mistretta","raw_affiliation_strings":["University of Wisconsin - Madison, United States;"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin - Madison, United States;","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5029188793"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06857392,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9413","issue":null,"first_page":"94132C","last_page":"94132C"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9991999864578247,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9951000213623047,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7353920936584473},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6974461674690247},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6971325278282166},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6694962978363037},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6544807553291321},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6539198160171509},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6474371552467346},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5679656267166138},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5518574714660645},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4863170087337494},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4843824803829193},{"id":"https://openalex.org/keywords/bilateral-filter","display_name":"Bilateral filter","score":0.483949214220047},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46101298928260803},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.42510196566581726},{"id":"https://openalex.org/keywords/median-filter","display_name":"Median filter","score":0.4141538143157959},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2865757942199707},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21124643087387085},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16879698634147644}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7353920936584473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6974461674690247},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6971325278282166},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6694962978363037},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6544807553291321},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6539198160171509},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6474371552467346},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5679656267166138},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5518574714660645},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4863170087337494},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4843824803829193},{"id":"https://openalex.org/C156140930","wikidata":"https://www.wikidata.org/wiki/Q860417","display_name":"Bilateral filter","level":3,"score":0.483949214220047},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46101298928260803},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.42510196566581726},{"id":"https://openalex.org/C55352655","wikidata":"https://www.wikidata.org/wiki/Q304247","display_name":"Median filter","level":4,"score":0.4141538143157959},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2865757942199707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21124643087387085},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16879698634147644},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2081545","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2081545","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,"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":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337338","display_name":"National Heart, Lung, and Blood Institute","ror":"https://ror.org/012pb6c26"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1985588167","https://openalex.org/W1996176493","https://openalex.org/W2005089986","https://openalex.org/W2041547938","https://openalex.org/W2064975723","https://openalex.org/W2079724595","https://openalex.org/W2095331884","https://openalex.org/W2099244020","https://openalex.org/W2103559027","https://openalex.org/W2107214962","https://openalex.org/W2115103087","https://openalex.org/W2150134853","https://openalex.org/W2796903329","https://openalex.org/W6618428896","https://openalex.org/W6675077989"],"related_works":["https://openalex.org/W2133587243","https://openalex.org/W1998728244","https://openalex.org/W233850645","https://openalex.org/W2082304850","https://openalex.org/W1984713107","https://openalex.org/W2765479697","https://openalex.org/W4285172075","https://openalex.org/W4386952422","https://openalex.org/W2998590421","https://openalex.org/W2094364519"],"abstract_inverted_index":{"<strong>Purpose</strong>:":[0],"The":[1,42,69,139,190],"purpose":[2],"of":[3,11,54,80,92,126,153,171,217,233],"this":[4],"work":[5],"is":[6,20,66,90,116,136,176,193],"to":[7,123,144,149,156,178,182,221],"improve":[8],"the":[9,23,46,50,55,85,88,97,109,157,163,169,186,205,214,218,231,234,238],"segmentation":[10,165,215],"interventional":[12],"devices":[13],"(e.g.":[14],"guidewires)":[15],"in":[16,57,96,240],"fluoroscopic":[17],"images.":[18],"This":[19,100],"required":[21],"for":[22,73,84,118,204],"real":[24,241],"time":[25,242],"3D":[26],"reconstruction":[27,37,235],"from":[28],"two":[29],"angiographic":[30],"views":[31],"where":[32],"noise":[33,47,150],"can":[34,101,200],"cause":[35],"severe":[36],"artifacts":[38,228],"and":[39,76,196,225,229],"incomplete":[40],"reconstruction.":[41],"proposed":[43,140,191],"method":[44],"reduces":[45,227],"while":[48],"enhancing":[49],"thin":[51],"line":[52,94],"structures":[53],"device":[56,164,219],"images":[58],"with":[59,160],"subtracted":[60],"background.":[61],"<strong>Methods</strong>:":[62],"A":[63],"two-step":[64],"approach":[65],"presented":[67],"here.":[68],"first":[70],"step":[71],"estimates,":[72],"each":[74],"pixel":[75,119],"a":[77,82,93,112,127,133,194],"given":[78],"number":[79,170],"directions,":[81],"measure":[83],"probability":[86],"that":[87,120],"point":[89],"part":[91,125],"segment":[95],"corresponding":[98],"direction.":[99],"be":[102,124,202],"done":[103],"efficiently":[104],"using":[105,166,185],"binary":[106],"masks.":[107],"In":[108],"second":[110],"step,":[111],"directional":[113],"filter":[114,135,159],"kernel":[115],"applied":[117],"are":[121],"assumed":[122],"line.":[128],"For":[129,162],"all":[130],"other":[131,222],"pixels":[132,175],"mean":[134],"used.":[137],"<strong>Results</strong>:":[138],"algorithm":[141,192],"was":[142],"able":[143],"achieve":[145],"an":[146],"average":[147],"contrast":[148],"ratio":[151],"(CNR)":[152],"6.3":[154],"compared":[155,181,220],"bilateral":[158,187],"5.8.":[161],"global":[167],"thresholding":[168],"missing":[172],"or":[173],"wrong":[174],"reduced":[177],"25":[179],"%":[180,184],"40":[183],"approach.":[188],"<strong>Conclusion</strong>:":[189],"simple":[195],"efficient":[197],"approach,":[198],"which":[199],"easily":[201],"parallelized":[203],"use":[206],"on":[207],"modern":[208],"graphics":[209],"processing":[210],"units.":[211],"It":[212],"improves":[213],"results":[216],"denoising":[223],"methods,":[224],"therefore":[226],"increases":[230],"quality":[232],"without":[236],"increasing":[237],"delay":[239],"applications":[243],"notably.":[244]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}