{"id":"https://openalex.org/W2166752577","doi":"https://doi.org/10.1109/tbme.2015.2448232","title":"Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy","display_name":"Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy","publication_year":2015,"publication_date":"2015-06-22","ids":{"openalex":"https://openalex.org/W2166752577","doi":"https://doi.org/10.1109/tbme.2015.2448232","mag":"2166752577","pmid":"https://pubmed.ncbi.nlm.nih.gov/26111385"},"language":"en","primary_location":{"id":"doi:10.1109/tbme.2015.2448232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbme.2015.2448232","pdf_url":null,"source":{"id":"https://openalex.org/S5240358","display_name":"IEEE Transactions on Biomedical Engineering","issn_l":"0018-9294","issn":["0018-9294","1558-2531"],"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 Biomedical Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100436460","display_name":"Guang Yang","orcid":"https://orcid.org/0000-0001-7344-7733"},"institutions":[{"id":"https://openalex.org/I165862685","display_name":"St George's, University of London","ror":"https://ror.org/040f08y74","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I165862685"]},{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Guang Yang","raw_affiliation_strings":["National Heart and Lung Institute, Imperial College London, London, U.K","Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Heart and Lung Institute, Imperial College London, London, U.K","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, U.K","institution_ids":["https://openalex.org/I165862685"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046947641","display_name":"Tahir Nawaz","orcid":"https://orcid.org/0000-0001-9511-8105"},"institutions":[{"id":"https://openalex.org/I180825142","display_name":"City, University of London","ror":"https://ror.org/04489at23","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I180825142"]},{"id":"https://openalex.org/I71052956","display_name":"University of Reading","ror":"https://ror.org/05v62cm79","country_code":"GB","type":"education","lineage":["https://openalex.org/I71052956"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tahir Nawaz","raw_affiliation_strings":["City University London","Computational Vision Group, University of Reading"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University London","institution_ids":["https://openalex.org/I180825142"]},{"raw_affiliation_string":"Computational Vision Group, University of Reading","institution_ids":["https://openalex.org/I71052956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012806070","display_name":"Thomas R. Barrick","orcid":"https://orcid.org/0000-0003-1783-3650"},"institutions":[{"id":"https://openalex.org/I165862685","display_name":"St George's, University of London","ror":"https://ror.org/040f08y74","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I165862685"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Thomas R. Barrick","raw_affiliation_strings":["St. George's, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"St. George's, University of London","institution_ids":["https://openalex.org/I165862685"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000805336","display_name":"Franklyn A. Howe","orcid":"https://orcid.org/0000-0002-7135-3351"},"institutions":[{"id":"https://openalex.org/I165862685","display_name":"St George's, University of London","ror":"https://ror.org/040f08y74","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I165862685"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Franklyn A. Howe","raw_affiliation_strings":["St. George's, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"St. George's, University of London","institution_ids":["https://openalex.org/I165862685"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037886447","display_name":"Greg Slabaugh","orcid":"https://orcid.org/0000-0003-4060-5226"},"institutions":[{"id":"https://openalex.org/I180825142","display_name":"City, University of London","ror":"https://ror.org/04489at23","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I180825142"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Greg Slabaugh","raw_affiliation_strings":["City University London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University London","institution_ids":["https://openalex.org/I180825142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0429,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.86305753,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"62","issue":"12","first_page":"2860","last_page":"2866"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.989799976348877,"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"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7712594270706177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7596517205238342},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7528171539306641},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.6946607828140259},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6719039678573608},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5749784708023071},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5700510740280151},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5076121091842651},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4768482446670532},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.4563009440898895},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.451882004737854},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43960168957710266},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.43513837456703186}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7712594270706177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7596517205238342},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7528171539306641},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.6946607828140259},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6719039678573608},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5749784708023071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5700510740280151},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5076121091842651},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4768482446670532},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.4563009440898895},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.451882004737854},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43960168957710266},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.43513837456703186}],"mesh":[{"descriptor_ui":"D000069558","descriptor_name":"Unsupervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069558","descriptor_name":"Unsupervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069558","descriptor_name":"Unsupervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069558","descriptor_name":"Unsupervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001932","descriptor_name":"Brain Neoplasms","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D001932","descriptor_name":"Brain Neoplasms","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D001932","descriptor_name":"Brain Neoplasms","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D001932","descriptor_name":"Brain Neoplasms","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":4,"locations":[{"id":"doi:10.1109/tbme.2015.2448232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbme.2015.2448232","pdf_url":null,"source":{"id":"https://openalex.org/S5240358","display_name":"IEEE Transactions on Biomedical Engineering","issn_l":"0018-9294","issn":["0018-9294","1558-2531"],"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 Biomedical Engineering","raw_type":"journal-article"},{"id":"pmid:26111385","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26111385","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on bio-medical engineering","raw_type":null},{"id":"pmh:oai:openaccess.city.ac.uk:14029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbme.2015.2448232>","pdf_url":null,"source":{"id":"https://openalex.org/S4306401940","display_name":"City Research Online (City University London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180825142","host_organization_name":"City, University of London","host_organization_lineage":["https://openalex.org/I180825142"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"},{"id":"pmh:oai:spiral.imperial.ac.uk:10044/1/24883","is_oa":false,"landing_page_url":"http://hdl.handle.net/10044/1/24883","pdf_url":null,"source":{"id":"https://openalex.org/S4306401396","display_name":"Spiral (Imperial College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I47508984","host_organization_name":"Imperial College London","host_organization_lineage":["https://openalex.org/I47508984"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2866","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G104446607","display_name":null,"funder_award_id":"C1459/A13303","funder_id":"https://openalex.org/F4320319985","funder_display_name":"Cancer Research UK"}],"funders":[{"id":"https://openalex.org/F4320319985","display_name":"Cancer Research UK","ror":"https://ror.org/054225q67"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W23958947","https://openalex.org/W1484777458","https://openalex.org/W1510992165","https://openalex.org/W1516725721","https://openalex.org/W1938234037","https://openalex.org/W1977813157","https://openalex.org/W1987971958","https://openalex.org/W1990383106","https://openalex.org/W1998613433","https://openalex.org/W2022441134","https://openalex.org/W2077144418","https://openalex.org/W2084681095","https://openalex.org/W2090237598","https://openalex.org/W2096947122","https://openalex.org/W2097172742","https://openalex.org/W2098314257","https://openalex.org/W2132984323","https://openalex.org/W2133093738","https://openalex.org/W2149901467","https://openalex.org/W2151879381","https://openalex.org/W2154053567","https://openalex.org/W2160382843","https://openalex.org/W2500050372","https://openalex.org/W4246406957","https://openalex.org/W6630765780"],"related_works":["https://openalex.org/W3027020613","https://openalex.org/W2016533837","https://openalex.org/W3167885074","https://openalex.org/W2892386716","https://openalex.org/W4306164210","https://openalex.org/W1998563493","https://openalex.org/W4313316311","https://openalex.org/W4362608745","https://openalex.org/W2383143032","https://openalex.org/W4390806283"],"abstract_inverted_index":{"Many":[0],"approaches":[1],"have":[2],"been":[3],"considered":[4],"for":[5],"automatic":[6],"grading":[7],"of":[8,13,51,56,66,108,122,129,134,154,172],"brain":[9,32,150],"tumors":[10],"by":[11],"means":[12],"pattern":[14],"recognition":[15],"with":[16,60,145],"magnetic":[17],"resonance":[18],"spectroscopy":[19],"(MRS).":[20],"Providing":[21],"an":[22,118,169],"improved":[23],"technique":[24],"which":[25,42],"can":[26],"assist":[27],"clinicians":[28],"in":[29,93],"accurately":[30],"identifying":[31],"tumor":[33],"grades":[34],"is":[35,43,138],"our":[36,94,135,166],"main":[37],"objective.":[38],"The":[39,106],"proposed":[40],"technique,":[41],"based":[44],"on":[45,157],"the":[46,64,67,72,77,132,139],"discrete":[47],"wavelet":[48,69],"transform":[49],"(DWT)":[50],"whole-spectral":[52,110],"or":[53,111,160],"subspectral":[54,112],"information":[55],"key":[57],"metabolites,":[58],"combined":[59,144],"unsupervised":[61,115,146],"learning,":[62],"inspects":[63],"separability":[65],"extracted":[68],"features":[70,174],"from":[71],"MRS":[73,89,159],"signal":[74],"to":[75,148,175],"aid":[76],"clustering.":[78],"In":[79],"total,":[80],"we":[81],"included":[82],"134":[83],"short":[84],"echo":[85],"time":[86],"single":[87],"voxel":[88],"spectra":[90],"(SV":[91],"MRS)":[92],"study":[95,141,167],"that":[96],"cover":[97],"normal":[98],"controls,":[99],"low":[100],"grade":[101,104],"and":[102,114,124],"high":[103],"tumors.":[105],"combination":[107],"DWT-based":[109],"analysis":[113],"clustering":[116,120,178],"achieved":[117],"overall":[119],"accuracy":[121],"94.8%":[123],"a":[125],"balanced":[126],"error":[127],"rate":[128],"7.8%.":[130],"To":[131],"best":[133],"knowledge,":[136],"it":[137],"first":[140],"using":[142,163],"DWT":[143],"learning":[147],"cluster":[149],"SV":[151,158],"MRS.":[152],"Instead":[153],"dimensionality":[155],"reduction":[156],"feature":[161],"selection":[162],"model":[164],"fitting,":[165],"provides":[168],"alternative":[170],"method":[171],"extracting":[173],"obtain":[176],"promising":[177],"results.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}