{"id":"https://openalex.org/W1929755393","doi":"https://doi.org/10.1109/sibgra.2004.1352936","title":"An adaptive approach to mammographic image denoising and enhancement","display_name":"An adaptive approach to mammographic image denoising and enhancement","publication_year":2004,"publication_date":"2004-11-30","ids":{"openalex":"https://openalex.org/W1929755393","doi":"https://doi.org/10.1109/sibgra.2004.1352936","mag":"1929755393"},"language":"en","primary_location":{"id":"doi:10.1109/sibgra.2004.1352936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sibgra.2004.1352936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing","raw_type":"proceedings-article"},"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/A5059279422","display_name":"Jacob Scharcanski","orcid":"https://orcid.org/0000-0002-9223-4693"},"institutions":[{"id":"https://openalex.org/I130442723","display_name":"Universidade Federal do Rio Grande do Sul","ror":"https://ror.org/041yk2d64","country_code":"BR","type":"education","lineage":["https://openalex.org/I130442723"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"J. Scharcanski","raw_affiliation_strings":["UFRGS Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil","Univ. Fed. do Rio Grande do Sul, Porto Alegre, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFRGS Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil","institution_ids":["https://openalex.org/I130442723"]},{"raw_affiliation_string":"Univ. Fed. do Rio Grande do Sul, Porto Alegre, Brazil","institution_ids":["https://openalex.org/I130442723"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111914462","display_name":"C.R. Jung","orcid":"https://orcid.org/0009-0002-7279-6397"},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"C.R. Jung","raw_affiliation_strings":["Ci\u00eancias Exatas e Tecnol\u00f3gicas, UNISINOS-Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Rio Grande do Sul, Brazil","UNISINOS - Universidade do Vale do Rio dos Sinos, Brasil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ci\u00eancias Exatas e Tecnol\u00f3gicas, UNISINOS-Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Rio Grande do Sul, Brazil","institution_ids":["https://openalex.org/I61722147"]},{"raw_affiliation_string":"UNISINOS - Universidade do Vale do Rio dos Sinos, Brasil","institution_ids":["https://openalex.org/I61722147"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.10630321,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"47","issue":null,"first_page":"2","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"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":0.9995999932289124,"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/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T10727","display_name":"Ultrasound Imaging and Elastography","score":0.9933000206947327,"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/wavelet","display_name":"Wavelet","score":0.6334221959114075},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5882817506790161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5764998197555542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.560185432434082},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5140336751937866},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.48762574791908264},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4744980037212372},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4657828211784363},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45585402846336365},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4508518576622009},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.42967987060546875},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.42076554894447327},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39273524284362793},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3805820047855377},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3691251873970032}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6334221959114075},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5882817506790161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5764998197555542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.560185432434082},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5140336751937866},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.48762574791908264},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4744980037212372},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4657828211784363},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45585402846336365},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4508518576622009},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.42967987060546875},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.42076554894447327},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39273524284362793},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3805820047855377},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3691251873970032},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sibgra.2004.1352936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sibgra.2004.1352936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W94936559","https://openalex.org/W1986276657","https://openalex.org/W2004217976","https://openalex.org/W2065391104","https://openalex.org/W2103763210","https://openalex.org/W2106200738","https://openalex.org/W2108394857","https://openalex.org/W2130551426","https://openalex.org/W2132984323","https://openalex.org/W2136017820","https://openalex.org/W2139122292","https://openalex.org/W2149925139","https://openalex.org/W2156706175","https://openalex.org/W2163612361","https://openalex.org/W2172182746","https://openalex.org/W4214540058","https://openalex.org/W4250507776","https://openalex.org/W4285719527","https://openalex.org/W6603762571","https://openalex.org/W6685280575"],"related_works":["https://openalex.org/W2387796150","https://openalex.org/W1990319978","https://openalex.org/W2362774332","https://openalex.org/W2386722878","https://openalex.org/W4225155218","https://openalex.org/W233850645","https://openalex.org/W4385546444","https://openalex.org/W2776327255","https://openalex.org/W4385377001","https://openalex.org/W3011587062"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"a":[3,43,68,89],"new":[4],"method":[5,123,147],"for":[6],"mammographic":[7,114],"image":[8,73,115],"noise":[9,24],"suppression":[10],"and":[11,42,60,85,129],"enhancement":[12,98,108],"based":[13,48],"on":[14,49],"the":[15,64,72,86,93,107,153],"wavelet":[16,65],"transform.":[17],"At":[18],"each":[19],"resolution,":[20],"coefficients":[21,31],"associated":[22,32],"with":[23,33],"are":[25,35,58],"modelled":[26,36],"by":[27,37,152],"Gaussian":[28,39],"random":[29,40],"variables;":[30],"edges":[34],"generalized":[38],"variables,":[41],"shrinkage":[44,53],"function":[45,99],"is":[46,76,111],"assembled":[47],"posterior":[50],"probabilities.":[51],"The":[52,97],"functions":[54],"at":[55],"consecutive":[56],"scales":[57],"combined,":[59],"then":[61],"applied":[62],"to":[63,102,126,143],"coefficients.":[66],"Given":[67],"resolution":[69],"of":[70,88],"analysis,":[71],"denoising":[74],"process":[75],"adaptive":[77],"(i.e.":[78],"does":[79],"not":[80],"require":[81],"further":[82],"parameter":[83,150],"adjustments),":[84],"selection":[87],"gain":[90],"factor":[91],"provides":[92],"desired":[94],"detail":[95],"enhancement.":[96],"was":[100],"designed":[101],"avoid":[103],"introducing":[104],"artifacts":[105],"in":[106,113,133],"process,":[109],"which":[110],"essential":[112],"analysis.":[116],"Our":[117],"preliminary":[118],"results":[119],"indicate":[120],"that":[121],"our":[122,146],"allows":[124],"us":[125],"detect":[127],"microcalcifications":[128],"other":[130,144],"suspicious":[131],"structures":[132],"situations":[134],"where":[135],"their":[136],"detection":[137],"would":[138],"be":[139],"difficult":[140],"otherwise.":[141],"Compared":[142],"approaches,":[145],"requires":[148],"less":[149],"adjustments":[151],"user.":[154]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}