{"id":"https://openalex.org/W2792713024","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302185","title":"A modified MRF segmentation of brain MR images","display_name":"A modified MRF segmentation of brain MR images","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2792713024","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302185","mag":"2792713024"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2017.8302185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5100349352","display_name":"Yuhao Zhang","orcid":"https://orcid.org/0000-0002-9856-436X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuhao Zhang","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China","University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101551571","display_name":"Pei L\u00fc","orcid":"https://orcid.org/0000-0002-6284-5805"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pei Lu","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100776309","display_name":"Xiaoyun Liu","orcid":"https://orcid.org/0000-0001-7083-5263"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyun Liu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046902140","display_name":"Shoujun Zhou","orcid":"https://orcid.org/0000-0003-3232-6796"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shoujun Zhou","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100349352"],"corresponding_institution_ids":["https://openalex.org/I150229711","https://openalex.org/I19820366","https://openalex.org/I4210145761"],"apc_list":null,"apc_paid":null,"fwci":0.2731,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66313834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"23","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9957000017166138,"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":0.9957000017166138,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9728999733924866,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6907483339309692},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6296342611312866},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6192507147789001},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6037839651107788},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5970979928970337},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4220598042011261}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6907483339309692},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6296342611312866},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6192507147789001},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6037839651107788},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5970979928970337},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4220598042011261}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2017.8302185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335055","display_name":"Health and Medical Research Fund","ror":"https://ror.org/03qh32912"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1507028917","https://openalex.org/W1554544485","https://openalex.org/W1579271636","https://openalex.org/W1972168313","https://openalex.org/W1981367467","https://openalex.org/W1989532447","https://openalex.org/W2009086942","https://openalex.org/W2020999234","https://openalex.org/W2047568922","https://openalex.org/W2105947467","https://openalex.org/W2114220616","https://openalex.org/W2117530999","https://openalex.org/W2136573752","https://openalex.org/W2162630772","https://openalex.org/W2166329820","https://openalex.org/W2166698530","https://openalex.org/W2797746357"],"related_works":["https://openalex.org/W1669643531","https://openalex.org/W2122581818","https://openalex.org/W1631910785","https://openalex.org/W2159066190","https://openalex.org/W2739874619","https://openalex.org/W2110230079","https://openalex.org/W2117933325","https://openalex.org/W2117664411","https://openalex.org/W1721780360","https://openalex.org/W1507266234"],"abstract_inverted_index":{"Image":[0],"noise":[1],"seriously":[2],"affects":[3],"the":[4,43,58,63,67,79,85,102,119,143,148,164,167,184,192,195,203,215],"accuracy":[5,232],"of":[6,74,81,95,147,170,177,194],"brain":[7,75,82,103,150],"magnetic":[8],"resonance":[9],"(MR)":[10],"image":[11,23,28,35,122,224],"classification.":[12],"The":[13,130,209],"traditional":[14],"Gaussian":[15],"mixture":[16],"model":[17,41,69,99,197],"(GMM)":[18],"is":[19,32,173,189,198,220],"widely":[20],"applied":[21],"in":[22,175,214],"segmentation,":[24],"which":[25,55],"uses":[26],"only":[27],"intensity":[29,123,205],"information":[30],"thus":[31],"susceptible":[33],"to":[34,100,141,158,202,223],"noise.":[36],"Markov":[37],"random":[38],"field":[39],"(MRF)":[40],"overcome":[42],"problem":[44],"by":[45],"using":[46],"a":[47,88],"priori":[48],"probability":[49,172],"for":[50,217],"local":[51,125,204],"smoothing":[52],"and":[53,97,114,124,206,226,230],"denoising,":[54],"depends":[56],"on":[57,93],"pixel":[59],"class":[60,181],"labelling":[61],"with":[62],"Gibbs":[64],"distribution.":[65],"However,":[66],"MRF":[68,98],"still":[70],"lacks":[71],"accurate":[72],"classification":[73],"tissue.":[76],"To":[77],"improve":[78],"segmentation":[80,90,231],"MR":[83],"image,":[84],"paper":[86],"proposes":[87],"joint":[89,171,196],"scheme":[91],"based":[92],"combination":[94],"GMM":[96,160,180],"classify":[101],"tissue":[104],"into":[105],"three":[106,149],"parts:":[107],"white":[108],"matter":[109,112],"(WM),":[110],"grey":[111],"(GM)":[113],"cerebrospinal":[115],"fluid":[116],"(CSF).":[117],"During":[118],"implementation,":[120],"both":[121],"neighbouring":[126],"voxels":[127],"are":[128,133],"considered.":[129],"main":[131],"steps":[132],"as":[134,233,235],"follows:":[135],"1)":[136],"we":[137],"use":[138,153],"K-means":[139],"estimation":[140,157],"acquire":[142],"initial":[144],"distribution":[145],"parameters":[146],"tissues;":[151],"then":[152,183],"Expectation":[154],"maximization":[155],"(EM)":[156],"obtain":[159],"parameters.":[161],"2)":[162],"For":[163],"spatial":[165,207],"voxels,":[166],"energy":[168,186],"function":[169],"change":[174],"terms":[176],"their":[178],"corresponding":[179],"information;":[182],"intensity-related":[185],"function's":[187],"component":[188],"controlled.":[190],"Therefore,":[191],"parameter":[193],"estimated":[199],"adaptively":[200],"according":[201],"information.":[208],"proposed":[210],"method":[211],"performs":[212],"well":[213,234],"experiments,":[216],"example,":[218],"it":[219],"not":[221],"sensitive":[222],"noise,":[225],"has":[227],"good":[228],"robustness":[229],"high":[236],"computational":[237],"efficiency.":[238]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
