{"id":"https://openalex.org/W4389314805","doi":"https://doi.org/10.1145/3627377.3627431","title":"Automatic Segmentation Method for the Nuclei Contour of High Resolution Rat Brain","display_name":"Automatic Segmentation Method for the Nuclei Contour of High Resolution Rat Brain","publication_year":2023,"publication_date":"2023-09-22","ids":{"openalex":"https://openalex.org/W4389314805","doi":"https://doi.org/10.1145/3627377.3627431"},"language":"en","primary_location":{"id":"doi:10.1145/3627377.3627431","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627377.3627431","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627431","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627431","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072001463","display_name":"Xiaofeng Xu","orcid":"https://orcid.org/0000-0002-9507-5712"},"institutions":[{"id":"https://openalex.org/I4210110718","display_name":"Nanyang Normal University","ror":"https://ror.org/01f7yer47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210110718"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofeng Xu","raw_affiliation_strings":["Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image,School of Computer Science and Technology, Nanyang Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-9507-5712","affiliations":[{"raw_affiliation_string":"Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image,School of Computer Science and Technology, Nanyang Normal University, China","institution_ids":["https://openalex.org/I4210110718"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115444845","display_name":"Wei Zheng","orcid":"https://orcid.org/0009-0005-3692-017X"},"institutions":[{"id":"https://openalex.org/I4210110718","display_name":"Nanyang Normal University","ror":"https://ror.org/01f7yer47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210110718"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zheng","raw_affiliation_strings":["Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image,School of Computer Science and Technology, Nanyang Normal University, China"],"raw_orcid":"https://orcid.org/0009-0005-3692-017X","affiliations":[{"raw_affiliation_string":"Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image,School of Computer Science and Technology, Nanyang Normal University, China","institution_ids":["https://openalex.org/I4210110718"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210110718"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20202234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"346","last_page":"350"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9966999888420105,"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/T12923","display_name":"Digital Image Processing Techniques","score":0.9843999743461609,"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":0.6939849853515625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6435126066207886},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6370336413383484},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.620384693145752},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.582284152507782},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4983234405517578},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.49296116828918457},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.47858619689941406},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4640677571296692},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4638851284980774},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38117480278015137},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2398412525653839},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13931825757026672}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6939849853515625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6435126066207886},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6370336413383484},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.620384693145752},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.582284152507782},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4983234405517578},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.49296116828918457},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.47858619689941406},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4640677571296692},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4638851284980774},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38117480278015137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2398412525653839},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13931825757026672},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"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/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627377.3627431","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627377.3627431","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627431","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3627377.3627431","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627377.3627431","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627431","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8700000047683716,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321883","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53"},{"id":"https://openalex.org/F4320323019","display_name":"Nanyang Normal University","ror":"https://ror.org/01f7yer47"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389314805.pdf","grobid_xml":"https://content.openalex.org/works/W4389314805.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W2015455780","https://openalex.org/W2016277672","https://openalex.org/W2037192432","https://openalex.org/W2038084250","https://openalex.org/W2040121225","https://openalex.org/W2043538065","https://openalex.org/W2049678062","https://openalex.org/W2050272066","https://openalex.org/W2053044392","https://openalex.org/W2079217673","https://openalex.org/W2089954740","https://openalex.org/W2107376108","https://openalex.org/W2141977394","https://openalex.org/W2163383667","https://openalex.org/W2963156446","https://openalex.org/W4324122459","https://openalex.org/W6631231365","https://openalex.org/W6652361072","https://openalex.org/W6662326984"],"related_works":["https://openalex.org/W2373481280","https://openalex.org/W1500268213","https://openalex.org/W4233585817","https://openalex.org/W2016045932","https://openalex.org/W1675950995","https://openalex.org/W2188882668","https://openalex.org/W2086619084","https://openalex.org/W2004379491","https://openalex.org/W2088323302","https://openalex.org/W2083140487"],"abstract_inverted_index":{"The":[0,166],"brain":[1,24,176],"consists":[2],"of":[3,17,23,63,71,118,127],"massive":[4],"nuclei":[5,120,133],"with":[6,75,112,121],"different":[7],"functions.":[8],"In":[9,144],"neuroscience":[10],"research,":[11],"the":[12,21,61,82,90,110,116,119,125,132,139],"precise":[13],"recognition":[14],"and":[15,46,97,142],"delineation":[16],"nucleus":[18],"boundaries":[19],"is":[20,52,86],"crux":[22],"atlas":[25],"illustration.":[26],"Here,":[27],"we":[28,67],"propose":[29,68],"a":[30,47,69,152],"method":[31,38,105,147],"based":[32,54],"on":[33,55,94],"Markov":[34],"Random":[35],"Field.":[36],"This":[37],"introduces":[39],"fractional":[40],"differentiation":[41],"into":[42],"texture":[43],"feature":[44],"extraction":[45],"new":[48],"potential":[49,171],"energy":[50],"function":[51],"defined":[53],"kernel":[56,155],"region":[57],"information.":[58],"To":[59],"handle":[60],"problem":[62],"large":[64],"data":[65,96,101],"volume,":[66],"strategy":[70],"dual":[72],"MRF":[73],"processing":[74],"down":[76],"sampling":[77],"for":[78,172],"pre":[79],"classification.":[80],"Finally,":[81],"fuzzy":[83],"entropy":[84],"criterion":[85],"used":[87],"to":[88],"optimize":[89],"segmentation":[91,167],"results.":[92],"Experiments":[93],"model":[95],"real":[98],"tissue":[99],"slice":[100],"show":[102],"that":[103],"this":[104,146],"can":[106,148],"not":[107,149],"only":[108,150],"segment":[109,151],"regions":[111],"obvious":[113],"differences":[114,123,137],"in":[115,124,156],"density":[117,126,136],"insignificant":[122,135],"nuclei,":[128],"but":[129,159],"also":[130,160],"divide":[131],"without":[134],"by":[138],"cell":[140],"shapes":[141],"textures.":[143],"addition,":[145],"specific":[153],"target":[154,164],"one":[157],"calculation,":[158],"simultaneously":[161],"partition":[162],"multiple":[163],"kernels.":[165],"algorithm":[168],"shows":[169],"great":[170],"illustrating":[173],"high-resolution":[174],"3D":[175],"atlases.":[177]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
