{"id":"https://openalex.org/W2962831904","doi":"https://doi.org/10.1109/access.2019.2930225","title":"Multimodal Neuroimaging Fusion in Nonsubsampled Shearlet Domain Using Location-Scale Distribution by Maximizing the High Frequency Subband Energy","display_name":"Multimodal Neuroimaging Fusion in Nonsubsampled Shearlet Domain Using Location-Scale Distribution by Maximizing the High Frequency Subband Energy","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2962831904","doi":"https://doi.org/10.1109/access.2019.2930225","mag":"2962831904"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2930225","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2930225","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08767909.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08767909.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068770723","display_name":"Emimal Jabason","orcid":"https://orcid.org/0000-0002-2537-9470"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Emimal Jabason","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","ORCiD"],"raw_orcid":"https://orcid.org/0000-0002-2537-9470","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068820891","display_name":"M. Omair Ahmad","orcid":"https://orcid.org/0000-0002-2924-6659"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. Omair Ahmad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","ORCiD"],"raw_orcid":"https://orcid.org/0000-0002-2924-6659","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013967994","display_name":"M.N.S. Swamy","orcid":"https://orcid.org/0000-0002-3989-5476"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. N. S. Swamy","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","ORCiD"],"raw_orcid":"https://orcid.org/0000-0002-3989-5476","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.5308,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.70654772,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"7","issue":null,"first_page":"97865","last_page":"97886"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9968000054359436,"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/shearlet","display_name":"Shearlet","score":0.844379186630249},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.681129515171051},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.6507893800735474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5896046757698059},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4781169295310974},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4746604263782501},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.4611416757106781},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44904130697250366},{"id":"https://openalex.org/keywords/brain-atlas","display_name":"Brain atlas","score":0.4294775128364563},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4134066104888916},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.217953622341156},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18941473960876465},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.1764412820339203},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.13412445783615112},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08416470885276794}],"concepts":[{"id":"https://openalex.org/C67795661","wikidata":"https://www.wikidata.org/wiki/Q17018993","display_name":"Shearlet","level":3,"score":0.844379186630249},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.681129515171051},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.6507893800735474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5896046757698059},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4781169295310974},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4746604263782501},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.4611416757106781},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44904130697250366},{"id":"https://openalex.org/C2780972224","wikidata":"https://www.wikidata.org/wiki/Q17047956","display_name":"Brain atlas","level":2,"score":0.4294775128364563},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4134066104888916},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.217953622341156},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18941473960876465},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.1764412820339203},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.13412445783615112},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08416470885276794},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2019.2930225","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2930225","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08767909.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:https://spectrum.library.concordia.ca:986092","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400871","display_name":"Spectrum Research Repository (Concordia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I60158472","host_organization_name":"Concordia University","host_organization_lineage":["https://openalex.org/I60158472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Article"},{"id":"pmh:oai:doaj.org/article:d7a2e2b55cf94e759aacf0117a2e536a","is_oa":true,"landing_page_url":"https://doaj.org/article/d7a2e2b55cf94e759aacf0117a2e536a","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 97865-97886 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2930225","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2930225","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08767909.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.7799999713897705,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2962831904.pdf","grobid_xml":"https://content.openalex.org/works/W2962831904.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W33520936","https://openalex.org/W59771946","https://openalex.org/W123101483","https://openalex.org/W784113155","https://openalex.org/W1669864993","https://openalex.org/W1971196148","https://openalex.org/W1972612668","https://openalex.org/W1986517950","https://openalex.org/W1987063402","https://openalex.org/W1997951181","https://openalex.org/W1999316653","https://openalex.org/W2016643491","https://openalex.org/W2040866042","https://openalex.org/W2067977604","https://openalex.org/W2073996439","https://openalex.org/W2078524519","https://openalex.org/W2091484864","https://openalex.org/W2097061348","https://openalex.org/W2108456818","https://openalex.org/W2115214993","https://openalex.org/W2115528090","https://openalex.org/W2117881455","https://openalex.org/W2127788404","https://openalex.org/W2128989367","https://openalex.org/W2132481658","https://openalex.org/W2133665775","https://openalex.org/W2140804420","https://openalex.org/W2146353910","https://openalex.org/W2152047336","https://openalex.org/W2154507364","https://openalex.org/W2167731203","https://openalex.org/W2208541107","https://openalex.org/W2252433728","https://openalex.org/W2289413700","https://openalex.org/W2411377185","https://openalex.org/W2461664044","https://openalex.org/W2473964400","https://openalex.org/W2474462684","https://openalex.org/W2532801510","https://openalex.org/W2590003272","https://openalex.org/W2620039506","https://openalex.org/W2620889673","https://openalex.org/W2746410086","https://openalex.org/W2746751201","https://openalex.org/W2766229547","https://openalex.org/W2808591023","https://openalex.org/W3157419554","https://openalex.org/W4211128380","https://openalex.org/W4244003036","https://openalex.org/W6721223673"],"related_works":["https://openalex.org/W2475220750","https://openalex.org/W2016759271","https://openalex.org/W2072476274","https://openalex.org/W2949924583","https://openalex.org/W2018897187","https://openalex.org/W4299617494","https://openalex.org/W1541049108","https://openalex.org/W2145495275","https://openalex.org/W2950003032","https://openalex.org/W1496339867"],"abstract_inverted_index":{"A":[0],"fusion":[1,27,49,126,184],"of":[2,38,54,90,220],"medical":[3,232],"imaging":[4,31],"data":[5,32],"obtained":[6],"from":[7,157,173],"different":[8],"modalities":[9],"plays":[10],"an":[11],"important":[12],"role":[13],"in":[14,139,209,234],"the":[15,35,55,63,86,96,116,121,130,137,140,158,174,180,188,206,210,231],"current":[16],"clinical":[17],"practice.":[18],"In":[19],"this":[20,105],"paper,":[21],"we":[22,73,103],"propose":[23],"a":[24,45,67,75,109,111,124,217],"novel":[25,46,125],"multimodal":[26,154,182],"algorithm":[28],"for":[29,120,128],"brain":[30],"based":[33,134],"on":[34,135,149],"statistical":[36],"properties":[37],"nonsubsampled":[39],"shearlet":[40],"transform":[41],"(NSST)":[42],"coefficients":[43,58,93,133],"and":[44,165,176,195,242],"energy":[47,138],"maximization":[48],"rule.":[50],"The":[51,226],"marginal":[52],"distributions":[53],"high-frequency":[56,141],"NSST":[57,92,132],"exhibit":[59],"heavier":[60],"tails":[61],"than":[62],"Gaussian":[64],"distribution.":[65],"As":[66],"consequence,":[68],"after":[69],"studying":[70],"its":[71],"characteristics,":[72],"use":[74],"heavy-tailed":[76],"probability":[77],"density":[78],"function,":[79],"student's":[80],"t":[81],"location-scale":[82],"distribution,":[83],"to":[84,107,114],"describe":[85],"highly":[87],"non-Gaussian":[88],"statistics":[89],"empirical":[91],"by":[94],"learning":[95],"parameters":[97],"using":[98],"maximum":[99,110],"likelihood":[100],"estimation.":[101],"Then,":[102,119],"employ":[104],"model":[106],"develop":[108],"posteriori":[112],"estimator":[113],"obtain":[115],"noise-free":[117],"coefficients.":[118],"first":[122],"time,":[123],"rule":[127],"obtaining":[129],"fused":[131,211],"maximizing":[136],"subbands":[142],"is":[143,171,198,202],"proposed.":[144],"Experiments":[145],"are":[146,213,223],"carried":[147],"out":[148],"fusing":[150],"two":[151],"or":[152],"more":[153,199,218],"neuroimages":[155],"taken":[156],"BrainWeb,":[159],"Alzheimer's":[160],"Disease":[161],"Neuroimaging":[162],"Initiative":[163],"(ADNI),":[164],"Whole":[166],"Brain":[167],"Atlas":[168],"databases.":[169],"It":[170,201],"seen":[172],"subjective":[175],"objective":[177],"results":[178],"that":[179,205],"proposed":[181,227],"neuroimaging":[183],"method":[185],"significantly":[186],"outperforms":[187],"state-of-the-art":[189],"methods":[190],"including":[191],"under":[192],"noisy":[193],"scenarios,":[194],"hence,":[196],"it":[197],"robust.":[200],"also":[203],"observed":[204],"signal":[207],"intensities":[208],"images":[212,222],"better":[214],"enhanced":[215],"when":[216],"number":[219],"source":[221],"being":[224],"fused.":[225],"technique":[228],"should":[229],"benefit":[230],"professionals":[233],"diagnosing":[235],"neurological":[236],"disorders,":[237],"such":[238],"as":[239],"Alzheimer,":[240],"epilepsy,":[241],"multiple":[243],"sclerosis.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}