{"id":"https://openalex.org/W1981017305","doi":"https://doi.org/10.1117/12.535676","title":"Tensor-scale-based diffusive filtering of medical images","display_name":"Tensor-scale-based diffusive filtering of medical images","publication_year":2004,"publication_date":"2004-05-12","ids":{"openalex":"https://openalex.org/W1981017305","doi":"https://doi.org/10.1117/12.535676","mag":"1981017305"},"language":"en","primary_location":{"id":"doi:10.1117/12.535676","doi":"https://doi.org/10.1117/12.535676","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.535676","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,"listed_in":["cwts-core","norway-1"],"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"},"volume":null,"type":"conference-paper","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/A5033203259","display_name":"Punam Kumar Saha","orcid":"https://orcid.org/0000-0003-1576-118X","observed_orcids":["https://orcid.org/0000-0003-1576-118X"]},"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":true,"raw_author_name":"Punam K. Saha","raw_affiliation_strings":["Univ. of Pennsylvania (United States)","University of Pennsylvania UNITED STATES"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Pennsylvania (United States)","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"University of Pennsylvania UNITED STATES","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5033203259"],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11136438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5370","issue":null,"first_page":"753","last_page":"753"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9990000128746033,"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":0.9990000128746033,"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":0.0005000000237487257,"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/T11019","display_name":"Image Enhancement Techniques","score":0.0003000000142492354,"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/anisotropic-diffusion","display_name":"anisotropic diffusion","score":0.9549999833106995},{"id":"https://openalex.org/keywords/signal-to-noise-ratio-improvement","display_name":"signal-to-noise ratio improvement","score":0.7450000047683716},{"id":"https://openalex.org/keywords/structural-anisotropy","display_name":"structural anisotropy","score":0.6320000290870667},{"id":"https://openalex.org/keywords/edge-preservation","display_name":"edge preservation","score":0.5809999704360962},{"id":"https://openalex.org/keywords/image-smoothing","display_name":"image smoothing","score":0.42399999499320984}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7479634284973145},{"id":"https://openalex.org/C113315163","wikidata":"https://www.wikidata.org/wiki/Q7625159","display_name":"Structure tensor","level":3,"score":0.657427191734314},{"id":"https://openalex.org/C141651230","wikidata":"https://www.wikidata.org/wiki/Q5337637","display_name":"Edge-preserving smoothing","level":4,"score":0.5665900707244873},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5312654376029968},{"id":"https://openalex.org/C85725439","wikidata":"https://www.wikidata.org/wiki/Q466686","display_name":"Anisotropy","level":2,"score":0.5123027563095093},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.5095586776733398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5038227438926697},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.479430615901947},{"id":"https://openalex.org/C74261601","wikidata":"https://www.wikidata.org/wiki/Q40112","display_name":"Ellipse","level":2,"score":0.4707544445991516},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.45494791865348816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44504570960998535},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.44496631622314453},{"id":"https://openalex.org/C121475858","wikidata":"https://www.wikidata.org/wiki/Q2735911","display_name":"Spatial filter","level":2,"score":0.4281584620475769},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42078715562820435},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4071807861328125},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.3428269624710083},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33200809359550476},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2628355622291565},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.24336490035057068},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22933289408683777},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.535676","doi":"https://doi.org/10.1117/12.535676","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.535676","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,"listed_in":["cwts-core","norway-1"],"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":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322892","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2172251945","https://openalex.org/W1999869503","https://openalex.org/W2164652569","https://openalex.org/W2005595608","https://openalex.org/W2030287849","https://openalex.org/W2010783536","https://openalex.org/W2325843214","https://openalex.org/W2949664540","https://openalex.org/W2043351120","https://openalex.org/W1549562450"],"abstract_inverted_index":{"Medical":[0],"imaging":[1],"techniques":[2],"often":[3],"suffer":[4],"from":[5],"low":[6],"signal-to-noise":[7],"ratio":[8,10],"(SNR)/signal-to-contrast":[9],"(CNR).":[11],"Filtering":[12],"is":[13,28,96,150,181,194,233],"an":[14,112],"effective":[15],"technique":[16],"commonly":[17],"used":[18,91],"to":[19,44,57,99,109],"improve":[20],"the":[21,37,68,79,106,120,151,155,168,190,218],"SNR/CNR.":[22],"A":[23,86],"fundamental":[24],"difficulty":[25],"in":[26,159],"filtering":[27,73,179,193,200],"its":[29,97,148,231],"smearing":[30],"effect":[31],"on":[32,221],"important":[33],"structures":[34,205],"along":[35,111,187,209],"with":[36,238],"suppression":[38],"of":[39,89,122,138,154,192,215,217],"noise.":[40],"Anisotropic":[41],"diffusion":[42],"attempts":[43],"partially":[45],"arrest":[46],"smoothing":[47,208],"across":[48,201],"boundaries,":[49],"which":[50],"are":[51,228],"recognized":[52],"by":[53,62,196,235],"high":[54],"gradients.":[55],"Improvements":[56],"this":[58,173],"approach":[59],"were":[60],"demonstrated":[61],"bringing":[63],"local":[64,128,139],"\"object":[65],"scale\"":[66],"into":[67],"framework":[69],"that":[70,133,163,198],"effects":[71],"intense":[72],"inside":[74,81,167],"homogeneous":[75,170],"regions":[76,82],"while":[77,206],"constricting":[78],"process":[80],"containing":[83],"fine":[84,204],"details.":[85],"major":[87],"drawback":[88],"previously":[90],"spherical":[92,240],"object":[93],"scale":[94,124],"concept":[95],"failure":[98],"recognize":[100],"structural":[101],"orientation":[102],"and":[103,143,165,203,224,230,242],"anisotropy.":[104,144],"Therefore,":[105],"method":[107,180,220],"failed":[108],"smooth":[110],"edge":[113],"or":[114,211],"a":[115,135,175],"thin":[116],"elongated":[117,212],"structure.":[118],"Recently,":[119],"notion":[121],"tensor":[123],"(in":[125],"short,":[126],"t-scale)-a":[127],"morphometric":[129],"descriptor-has":[130],"been":[131],"introduced":[132],"provides":[134],"unified":[136],"description":[137],"structure":[140],"size,":[141],"orientation,":[142],"At":[145],"any":[146,185,188],"point":[147,164],"t-scale":[149,197],"parametric":[152],"representation":[153],"largest":[156],"ellipse":[157],"(ellipsoid":[158],"3D)":[160],"centered":[161],"at":[162,184],"contained":[166],"same":[169],"region.":[171],"In":[172],"paper,":[174],"new":[176],"anisotropic":[177],"diffusive":[178,244],"presented,":[182],"wherein":[183],"location,":[186],"direction,":[189],"degree":[191],"governed":[195],"restricts":[199],"boundaries":[202],"encouraging":[207],"edges":[210],"structures.":[213],"Results":[214],"applications":[216],"proposed":[219],"several":[222],"phantoms":[223],"real":[225],"medical":[226],"images":[227],"presented":[229],"effectiveness":[232],"evaluated":[234],"comparing":[236],"it":[237],"both":[239],"scale-based":[241],"ordinary":[243],"filtering.":[245]},"counts_by_year":[],"updated_date":"2026-09-22T06:50:58.402377","created_date":"2016-06-24T00:00:00"}
