{"id":"https://openalex.org/W2996244296","doi":"https://doi.org/10.1109/tencon.2019.8929346","title":"Image Quality Assessment, Denoising and Comparative Analysis using Filters for C-arm X-ray Images","display_name":"Image Quality Assessment, Denoising and Comparative Analysis using Filters for C-arm X-ray Images","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2996244296","doi":"https://doi.org/10.1109/tencon.2019.8929346","mag":"2996244296"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929346","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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/A5029575721","display_name":"Snehal Yadav","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133649","display_name":"Philips (India)","ror":"https://ror.org/0435vfk93","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210122849","https://openalex.org/I4210133649"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Snehal Yadav","raw_affiliation_strings":["HIC R&D, Philips Student of College of Engineering, Pune, India"],"affiliations":[{"raw_affiliation_string":"HIC R&D, Philips Student of College of Engineering, Pune, India","institution_ids":["https://openalex.org/I4210133649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070446104","display_name":"Saket Kulkarni","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133649","display_name":"Philips (India)","ror":"https://ror.org/0435vfk93","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210122849","https://openalex.org/I4210133649"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Saket Kulkarni","raw_affiliation_strings":["IGT systems, HIC R&D, Philips, Pune, India"],"affiliations":[{"raw_affiliation_string":"IGT systems, HIC R&D, Philips, Pune, India","institution_ids":["https://openalex.org/I4210133649"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013176709","display_name":"Rashmika Patole","orcid":"https://orcid.org/0000-0001-7247-0296"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rashmika Patole","raw_affiliation_strings":["ENTC Department, College of Engineering, Pune, India"],"affiliations":[{"raw_affiliation_string":"ENTC Department, College of Engineering, Pune, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029575721"],"corresponding_institution_ids":["https://openalex.org/I4210133649"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.46545803,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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":0.9994999766349792,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6539435982704163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6418370008468628},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.6192401647567749},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5663279891014099},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5407388806343079},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5350946187973022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40744277834892273},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3501160144805908}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6539435982704163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6418370008468628},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.6192401647567749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5663279891014099},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5407388806343079},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5350946187973022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40744277834892273},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3501160144805908}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929346","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1591158573","https://openalex.org/W1973959125","https://openalex.org/W2006790618","https://openalex.org/W2025935185","https://openalex.org/W2040269599","https://openalex.org/W2137969878","https://openalex.org/W2139058413","https://openalex.org/W2164489410","https://openalex.org/W2368438146","https://openalex.org/W3004958964","https://openalex.org/W3202085623","https://openalex.org/W4300920262","https://openalex.org/W6684403973","https://openalex.org/W6773919120"],"related_works":["https://openalex.org/W2895947835","https://openalex.org/W2087258800","https://openalex.org/W2810018092","https://openalex.org/W2387428419","https://openalex.org/W2098237619","https://openalex.org/W1974034585","https://openalex.org/W2386722878","https://openalex.org/W2353444452","https://openalex.org/W2001438600","https://openalex.org/W2499707420"],"abstract_inverted_index":{"C-arm":[0,80],"is":[1,87,153,181],"a":[2,67],"fluoroscopy":[3,29],"based":[4],"medical":[5],"imaging":[6],"device,":[7],"which":[8],"has":[9,21],"various":[10,31],"operational":[11],"parameters":[12,103],"(dose":[13],"levels,":[14,136],"power":[15],"applied":[16,137],"and":[17,56,73,107,140,162],"frame":[18,141],"rate)":[19],"that":[20,42],"some":[22],"effect":[23,126],"on":[24,89,127],"the":[25,53,59],"image":[26,54,82,92,146,151,173],"quality.":[27],"X-ray":[28,81],"exhibits":[30],"types":[32],"of":[33,50,61,99,133,149,165,172,177],"noise":[34,51,110,116,120,128],"(quantum,":[35],"Gaussian,":[36],"speckle,":[37],"photon,":[38],"Poisson":[39],"noise,":[40],"etc.)":[41],"must":[43],"be":[44],"reduced":[45],"in":[46],"real":[47],"time.":[48],"Presence":[49],"degrades":[52],"quality":[55,71],"thus":[57],"decreases":[58],"performance":[60,180],"subsequent":[62],"application.":[63],"We":[64],"have":[65],"developed":[66],"method":[68],"for":[69,76,114,144,170],"statistical":[70],"assessment":[72],"denoising":[74,171],"technique":[75],"flat":[77],"field":[78],"detected":[79],"sequence.":[83,147,174],"The":[84],"analysis":[85],"approach":[86],"tested":[88],"84":[90],"x-ray":[91],"sequences":[93],"acquired":[94],"from":[95],"homogeneous":[96],"phantoms":[97],"(PMMA)":[98],"different":[100,131,166,178],"widths.":[101],"Statistical":[102],"(mean,":[104],"standard":[105],"deviation":[106],"signal":[108],"to":[109,124,130],"ratio)":[111],"are":[112,122,168],"considered":[113,143],"generating":[115],"profile.":[117],"These":[118],"generated":[119],"profiles":[121],"used":[123],"study":[125],"due":[129],"widths":[132],"phantom,":[134],"dose":[135],"kV":[138],"power,":[139],"rate":[142],"respective":[145],"Denoising":[148],"these":[150],"sequence":[152],"done":[154],"using":[155],"spatial":[156],"domain":[157],"filters.":[158],"Conventional":[159],"mean,":[160],"Gaussian":[161],"median":[163],"filters":[164],"kernels":[167],"chosen":[169],"Comparative":[175],"results":[176],"filter":[179],"also":[182],"studied.":[183]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}