{"id":"https://openalex.org/W3003201377","doi":"https://doi.org/10.1117/12.2559545","title":"FMRI image segmentation based on hidden Markov random field with directional statistics observation model","display_name":"FMRI image segmentation based on hidden Markov random field with directional statistics observation model","publication_year":2020,"publication_date":"2020-01-31","ids":{"openalex":"https://openalex.org/W3003201377","doi":"https://doi.org/10.1117/12.2559545","mag":"3003201377"},"language":"en","primary_location":{"id":"doi:10.1117/12.2559545","doi":"https://doi.org/10.1117/12.2559545","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2559545","pdf_url":null,"source":{"id":"https://openalex.org/S4306419647","display_name":"International Conference on Machine Vision","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twelfth International Conference on Machine Vision (ICMV 2019)","raw_type":"proceedings-article"},"parent_works":[],"lineage":["https://openalex.org/W3003201377"],"child_works_count":0,"type":"conference-paper","is_traditional":true,"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/A5001490316","display_name":"\u041e\u043b\u0435\u0433 \u041b\u0443\u043a\u0430\u0448\u0435\u043d\u043a\u043e","orcid":"https://orcid.org/0000-0002-2809-5481","observed_orcids":["https://orcid.org/0000-0002-2809-5481"]},"institutions":[{"id":"https://openalex.org/I4210152247","display_name":"Karelian Research Centre","ror":"https://ror.org/05wgt8358","country_code":"RU","type":"facility","lineage":["https://openalex.org/I1313323035","https://openalex.org/I4210152247","https://openalex.org/I4210160366"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Oleg Lukashenko","raw_affiliation_strings":["IAMR KarRC RAS (Russian Federation)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAMR KarRC RAS (Russian Federation)","institution_ids":["https://openalex.org/I4210152247"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076945000","display_name":"Sergey Chernyaev","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I197524780","display_name":"Petrozavodsk State University","ror":"https://ror.org/0176aa147","country_code":"RU","type":"education","lineage":["https://openalex.org/I197524780"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Sergey Chernyaev","raw_affiliation_strings":["Petrozavodsk State Univ. (Russian Federation)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Petrozavodsk State Univ. (Russian Federation)","institution_ids":["https://openalex.org/I197524780"]}]}],"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":0,"citation_normalized_percentile":{"value":0.00762222,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"119","last_page":"119"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","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/T10241","display_name":"Functional Brain Connectivity Studies","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.0,"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/hidden-markov-random-field","display_name":"hidden Markov random field","score":0.9340000152587891},{"id":"https://openalex.org/keywords/directional-statistics","display_name":"circular statistics","score":0.8180000185966492},{"id":"https://openalex.org/keywords/von-mises-fisher-distribution","display_name":"von Mises-Fisher distribution","score":0.9990000128746033},{"id":"https://openalex.org/keywords/3d-image-segmentation","display_name":"3D image segmentation","score":0.75},{"id":"https://openalex.org/keywords/bayesian-segmentation","display_name":"Bayesian segmentation","score":0.6729999780654907},{"id":"https://openalex.org/keywords/mean-field-approximation","display_name":"mean-field approximation","score":0.9900000095367432},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.7419999837875366},{"id":"https://openalex.org/keywords/prior-distributions","display_name":"prior distributions","score":0.3720000088214874}],"concepts":[{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.7805156707763672},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6944295167922974},{"id":"https://openalex.org/C198138136","wikidata":"https://www.wikidata.org/wiki/Q7941490","display_name":"von Mises distribution","level":4,"score":0.5996314287185669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5876519680023193},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5757373571395874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5386260747909546},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5351674556732178},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4983663558959961},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.494274377822876},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.47631701827049255},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4660554826259613},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.44010475277900696},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.42771533131599426},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4275178611278534},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.41248786449432373},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4123706817626953},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.38461846113204956},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32667890191078186},{"id":"https://openalex.org/C155165730","wikidata":"https://www.wikidata.org/wiki/Q1319519","display_name":"von Mises yield criterion","level":3,"score":0.25118982791900635},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23016762733459473},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2559545","doi":"https://doi.org/10.1117/12.2559545","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2559545","pdf_url":null,"source":{"id":"https://openalex.org/S4306419647","display_name":"International Conference on Machine Vision","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twelfth International Conference on Machine Vision (ICMV 2019)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2991001665","https://openalex.org/W2048771405","https://openalex.org/W2136573752","https://openalex.org/W3181529191","https://openalex.org/W2284772414","https://openalex.org/W1545349697","https://openalex.org/W2052691802","https://openalex.org/W1524649378","https://openalex.org/W2137516685","https://openalex.org/W2171493089"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3,56],"consider":[4],"the":[5,14,23,31,34,44,53,58,63,67,77],"problem":[6],"of":[7,9,36],"segmentation":[8],"three-dimensional":[10],"fMRI":[11],"images":[12],"within":[13],"Bayesian":[15],"framework":[16],"with":[17],"Markov":[18],"Random":[19],"Field":[20],"(MRF)":[21],"as":[22,30],"prior":[24],"distribution":[25,29],"and":[26,43],"von":[27],"Mises-Fisher":[28],"likelihood.":[32],"Usually,":[33],"learning":[35],"such":[37],"models":[38],"is":[39,47],"a":[40],"complicated":[41],"task":[42],"exact":[45],"inference":[46,64],"impossible":[48],"in":[49,66],"practice.":[50],"To":[51],"fit":[52],"proposed":[54,78],"model,":[55],"apply":[57],"mean":[59],"field":[60],"approximation":[61],"on":[62],"step":[65],"EM":[68],"algorithm.":[69],"Some":[70],"numerical":[71],"examples":[72],"are":[73],"presented":[74],"to":[75],"illustrate":[76],"method.":[79]},"counts_by_year":[],"updated_date":"2026-10-05T09:50:58.205359","created_date":"2020-02-07T00:00:00"}
