{"id":"https://openalex.org/W2933486688","doi":"https://doi.org/10.1109/tim.2019.2902808","title":"Multimodal Medical Image Sensor Fusion Model Using Sparse K-SVD Dictionary Learning in Nonsubsampled Shearlet Domain","display_name":"Multimodal Medical Image Sensor Fusion Model Using Sparse K-SVD Dictionary Learning in Nonsubsampled Shearlet Domain","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2933486688","doi":"https://doi.org/10.1109/tim.2019.2902808","mag":"2933486688"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2019.2902808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2019.2902808","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5047012112","display_name":"Sneha Singh","orcid":"https://orcid.org/0000-0002-1728-982X"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sneha Singh","raw_affiliation_strings":["Department of Electrical Engineering, IIT Roorkee, Roorkee, India"],"raw_orcid":"https://orcid.org/0000-0002-1728-982X","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, IIT Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102731555","display_name":"R. S. Anand","orcid":"https://orcid.org/0000-0002-2031-0538"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R. S. Anand","raw_affiliation_strings":["Department of Electrical Engineering, IIT Roorkee, Roorkee, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, IIT Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154851008"],"apc_list":null,"apc_paid":null,"fwci":5.9232,"has_fulltext":false,"cited_by_count":72,"citation_normalized_percentile":{"value":0.96335166,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"69","issue":"2","first_page":"593","last_page":"607"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"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/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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9994000196456909,"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/shearlet","display_name":"Shearlet","score":0.8952468633651733},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7517198920249939},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.6929823160171509},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6759133338928223},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5914939641952515},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5879077315330505},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5351264476776123},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5279383063316345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5237788558006287},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.497010737657547},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4712916910648346},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.46985924243927},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4267650544643402},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34434613585472107}],"concepts":[{"id":"https://openalex.org/C67795661","wikidata":"https://www.wikidata.org/wiki/Q17018993","display_name":"Shearlet","level":3,"score":0.8952468633651733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7517198920249939},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.6929823160171509},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6759133338928223},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5914939641952515},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5879077315330505},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5351264476776123},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5279383063316345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5237788558006287},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.497010737657547},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4712916910648346},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.46985924243927},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4267650544643402},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34434613585472107},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2019.2902808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2019.2902808","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1652138977","https://openalex.org/W1669864993","https://openalex.org/W1906374873","https://openalex.org/W1964189664","https://openalex.org/W1974774078","https://openalex.org/W1976272929","https://openalex.org/W1976709621","https://openalex.org/W1982075130","https://openalex.org/W1986231131","https://openalex.org/W1996278907","https://openalex.org/W1997951181","https://openalex.org/W1999034606","https://openalex.org/W1999316653","https://openalex.org/W2010272715","https://openalex.org/W2010515061","https://openalex.org/W2014886478","https://openalex.org/W2035848186","https://openalex.org/W2038420319","https://openalex.org/W2066667210","https://openalex.org/W2069127385","https://openalex.org/W2071604881","https://openalex.org/W2075654948","https://openalex.org/W2081195086","https://openalex.org/W2082506960","https://openalex.org/W2089809277","https://openalex.org/W2091484864","https://openalex.org/W2092841641","https://openalex.org/W2095003058","https://openalex.org/W2108283046","https://openalex.org/W2114207195","https://openalex.org/W2116702374","https://openalex.org/W2146353910","https://openalex.org/W2147347517","https://openalex.org/W2151693816","https://openalex.org/W2159269332","https://openalex.org/W2160547390","https://openalex.org/W2163398148","https://openalex.org/W2168783614","https://openalex.org/W2170539513","https://openalex.org/W2339983363","https://openalex.org/W2340035164","https://openalex.org/W2470270897","https://openalex.org/W2474462684","https://openalex.org/W2519745404","https://openalex.org/W2520768522","https://openalex.org/W2544972162","https://openalex.org/W2561740325","https://openalex.org/W2768166594","https://openalex.org/W2772698944","https://openalex.org/W2778414125","https://openalex.org/W2783015445","https://openalex.org/W2783534102","https://openalex.org/W2794692403","https://openalex.org/W2891209129","https://openalex.org/W3103757879","https://openalex.org/W3125032682","https://openalex.org/W4300025484","https://openalex.org/W6637156639","https://openalex.org/W6748598748"],"related_works":["https://openalex.org/W2087347181","https://openalex.org/W2117383856","https://openalex.org/W2347811001","https://openalex.org/W4323566558","https://openalex.org/W2567113052","https://openalex.org/W1996627459","https://openalex.org/W2356070877","https://openalex.org/W2367279519","https://openalex.org/W2975989341","https://openalex.org/W2908488917"],"abstract_inverted_index":{"Multimodal":[0],"medical":[1,38],"image":[2,215],"sensor":[3],"fusion":[4,87],"(MMISF)":[5],"has":[6],"a":[7,76,85,124,154,189],"significant":[8],"role":[9],"for":[10,89],"better":[11,241],"visualization":[12],"of":[13,43,73,79,100,139,181,192],"the":[14,20,37,70,80,118,135,159,165,176,182,196,228],"diagnostic":[15],"statistics":[16],"computed":[17,203,210],"by":[18,59,117,164,174,188],"integrating":[19],"vital":[21],"information":[22,144],"taken":[23,52],"from":[24,53],"input":[25,101],"source":[26,113,140],"images":[27,51,102,114,171,244],"acquired":[28],"using":[29],"multimodal":[30,90],"imaging":[31,55],"sensors.":[32],"The":[33,148,179],"MMISF":[34,184,230],"also":[35],"helps":[36],"professionals":[39],"in":[40],"precise":[41],"diagnosis":[42],"several":[44,128,235],"critical":[45],"diseases":[46],"and":[47,127,145,158,209],"its":[48,108],"treatment.":[49],"Often,":[50],"different":[54,197],"sensors":[56],"are":[57,162,172],"degraded":[58],"noise":[60,74],"interferences":[61],"during":[62],"acquisition":[63],"or":[64],"data":[65,216],"transmission":[66],"that":[67,227],"leads":[68],"to":[69,96,132,234],"false":[71],"perception":[72],"as":[75,238],"useful":[77],"feature":[78],"image.":[81],"This":[82],"paper":[83],"presents":[84],"novel":[86],"framework":[88],"neurological":[91,214],"images,":[92],"which":[93],"is":[94,186,225,232],"able":[95],"capture":[97],"small-scale":[98],"details":[99],"with":[103,153,245],"original":[104],"structural":[105],"details.":[106,147],"In":[107],"procedural":[109],"steps,":[110],"at":[111],"first,":[112],"get":[115,151],"decomposed":[116],"nonsubsampled":[119],"shearlet":[120],"transform":[121],"(NSST)":[122],"into":[123],"low-frequency":[125],"(lf)":[126],"high-frequency":[129],"(hf)":[130],"components":[131,161],"separate":[133],"out":[134],"two":[136],"basic":[137],"characteristics":[138],"image,":[141],"i.e.,":[142],"principal":[143],"edge":[146],"lf":[149],"layers":[150],"fused":[152,170,243],"sparse":[155],"representation-based":[156],"model,":[157],"hf":[160],"merged":[163],"guided":[166],"filtering-based":[167],"approach.":[168],"Finally,":[169],"reconstructed":[173],"employing":[175],"inverse":[177],"NSST.":[178],"superiority":[180],"proposed":[183,229],"approach":[185,231],"confirmed":[187],"large":[190],"extent":[191],"analytical":[193],"experimentations":[194],"on":[195,219],"real":[198],"magnetic":[199,205,212],"resonance":[200,213],"single-photon":[201],"emission":[202,207],"tomography,":[204],"resonance-positron":[206],"tomography":[208,211],"sets.":[217],"Based":[218],"all":[220],"these":[221],"experimental":[222],"results,":[223],"it":[224,239],"stated":[226],"superior":[233],"other":[236],"approaches":[237],"produces":[240],"visually":[242],"improved":[246],"computational":[247],"measures.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":14}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}