{"id":"https://openalex.org/W2791219509","doi":"https://doi.org/10.1117/12.2293449","title":"Classifying Alzheimer's disease using probability distribution distance of fractional anisotropy and trace from diffusion tensor imaging in combination with whole-brain segmentations","display_name":"Classifying Alzheimer's disease using probability distribution distance of fractional anisotropy and trace from diffusion tensor imaging in combination with whole-brain segmentations","publication_year":2018,"publication_date":"2018-03-12","ids":{"openalex":"https://openalex.org/W2791219509","doi":"https://doi.org/10.1117/12.2293449","mag":"2791219509"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293449","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-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/A5101749551","display_name":"Yuanyuan Wei","orcid":"https://orcid.org/0000-0003-0668-3757"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":true,"raw_author_name":"Yuanyuan Wei","raw_affiliation_strings":["Carnegie Mellon Univ. (United States)","Sun Yat-Sen Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon Univ. (United States)","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Sun Yat-Sen Univ. (China)","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100604103","display_name":"Zhibin Chen","orcid":"https://orcid.org/0000-0002-2865-8416"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Zhibin Chen","raw_affiliation_strings":["Carnegie Mellon Univ. (United States)","Sun Yat-Sen Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon Univ. (United States)","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Sun Yat-Sen Univ. (China)","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001406512","display_name":"Xiaoying Tang","orcid":"https://orcid.org/0000-0002-9610-0318"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210129003","display_name":"SYSU-CMU International Joint Research Institute","ror":"https://ror.org/02w30ae27","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210129003"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoying Tang","raw_affiliation_strings":["Sun Yat-Sen Univ. (China)","Sun Yat-Sen Univ.-Carnegie Mellon Univ. Shunde International Joint Research Institute (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen Univ. (China)","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Sun Yat-Sen Univ.-Carnegie Mellon Univ. Shunde International Joint Research Institute (China)","institution_ids":["https://openalex.org/I4210129003","https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101749551"],"corresponding_institution_ids":["https://openalex.org/I157773358","https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.3597,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62510211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"44","issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"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/T11097","display_name":"Cerebral Palsy and Movement Disorders","score":0.9487000107765198,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/T10086","display_name":"Alzheimer's disease research and treatments","score":0.9287999868392944,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7044832706451416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6946183443069458},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6525813341140747},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6183130741119385},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5981453061103821},{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"Diffusion MRI","score":0.5807749032974243},{"id":"https://openalex.org/keywords/fractional-anisotropy","display_name":"Fractional anisotropy","score":0.5699092149734497},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.483141154050827},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.48263484239578247},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4645867645740509},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.41738444566726685},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2435772716999054},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09543991088867188}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7044832706451416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6946183443069458},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6525813341140747},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6183130741119385},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5981453061103821},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.5807749032974243},{"id":"https://openalex.org/C89916169","wikidata":"https://www.wikidata.org/wiki/Q17014600","display_name":"Fractional anisotropy","level":4,"score":0.5699092149734497},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.483141154050827},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.48263484239578247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4645867645740509},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.41738444566726685},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2435772716999054},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09543991088867188},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","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/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"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":1,"locations":[{"id":"doi:10.1117/12.2293449","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.699999988079071,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1545850453","https://openalex.org/W1560724230","https://openalex.org/W1926586119","https://openalex.org/W1981367467","https://openalex.org/W1989217296","https://openalex.org/W1997222702","https://openalex.org/W1998049664","https://openalex.org/W2066974932","https://openalex.org/W2106931873","https://openalex.org/W2110431535","https://openalex.org/W2123261378","https://openalex.org/W2143826137","https://openalex.org/W2156220037","https://openalex.org/W2171380313","https://openalex.org/W2171831801","https://openalex.org/W2398671403","https://openalex.org/W2573753975","https://openalex.org/W2595575783","https://openalex.org/W3120421331","https://openalex.org/W4206679801","https://openalex.org/W4239510810","https://openalex.org/W6632659634","https://openalex.org/W6640101315","https://openalex.org/W6734708421","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W2052589448","https://openalex.org/W2765337000","https://openalex.org/W3031573373","https://openalex.org/W2017539237","https://openalex.org/W3104072235","https://openalex.org/W2607776059","https://openalex.org/W2802561361","https://openalex.org/W2391447249","https://openalex.org/W2312955079","https://openalex.org/W2487470953"],"abstract_inverted_index":{"Using":[0],"diffusion":[1],"tensor":[2],"imaging":[3],"(DTI),":[4],"we":[5],"developed":[6],"and":[7,22,29,57,70,113,118,138,158],"validated":[8],"an":[9,174],"automated":[10,124],"classification":[11,151],"procedure":[12],"for":[13,123],"Alzheimer\u2019s":[14],"disease":[15],"(AD);":[16],"specifically,":[17],"DTI-derived":[18],"fractional":[19],"anisotropy":[20],"(FA)":[21],"trace":[23,58,146],"images":[24],"from":[25],"22":[26],"AD":[27,176],"subjects":[28,34],"15":[30],"healthy":[31],"control":[32],"(HC)":[33],"were":[35,48,89],"used.":[36],"A":[37],"total":[38],"of":[39,42,44,55,62,83,87,109,136,144],"four":[40],"types":[41],"region":[43],"interest":[45],"(ROI)-based":[46],"features":[47],"tested,":[49],"including":[50],"the":[51,85,96,107,110,134,141,145,149,154,159,162],"probability":[52],"distribution":[53,73],"distances":[54],"FA":[56],"images,":[59],"within":[60],"each":[61],"162":[63],"whole-brain":[64],"segmented":[65],"ROIs,":[66],"under":[67,161],"both":[68],"discrete":[69],"continuous":[71,76,142],"intensity":[72],"modeling.":[74],"The":[75],"modeling":[77],"was":[78],"conducted":[79],"through":[80],"a":[81],"mixture":[82],"Gaussians,":[84],"parameters":[86],"which":[88],"estimated":[90],"using":[91,133],"maximum":[92],"likelihood":[93],"estimation":[94],"via":[95],"expectation-maximization":[97],"algorithm.":[98],"We":[99],"used":[100],"principal":[101],"component":[102],"analysis":[103,117],"(PCA)":[104],"to":[105,127],"reduce":[106],"dimension":[108],"feature":[111],"space":[112],"then":[114],"linear":[115,139],"discriminant":[116],"support":[119],"vector":[120],"machine":[121],"(SVM)":[122],"classification.":[125],"According":[126],"our":[128],"10-times":[129],"10-fold":[130],"cross-validation":[131],"experiments,":[132],"combination":[135],"PCA":[137],"SVM,":[140],"distance":[143],"image":[147],"yielded":[148],"best":[150],"performance":[152],"with":[153],"accuracy":[155],"being":[156,167],"87.84%&plusmn;3.43%":[157],"area":[160],"receiver":[163],"operating":[164],"characteristic":[165],"curve":[166],"0.9121&plusmn;0.0176,":[168],"indicating":[169],"its":[170],"great":[171],"potential":[172],"as":[173],"effective":[175],"biomarker.":[177]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}
