{"id":"https://openalex.org/W2756380987","doi":"https://doi.org/10.1109/icip.2017.8297002","title":"Modeling structural dissimilarity based on shape embodiment for cell segmentation","display_name":"Modeling structural dissimilarity based on shape embodiment for cell segmentation","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2756380987","doi":"https://doi.org/10.1109/icip.2017.8297002","mag":"2756380987"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8297002","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5109226983","display_name":"Hyun-Gyu Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyun-Gyu Lee","raw_affiliation_strings":["Department of Computer Science and Engineering, INHA University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, INHA University","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073137941","display_name":"Adiba Orzikulova","orcid":"https://orcid.org/0009-0006-4085-2701"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Adiba Orzikulova","raw_affiliation_strings":["Department of Computer Science and Engineering, INHA University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, INHA University","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019858294","display_name":"Bo-Gyu Park","orcid":null},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Bo-Gyu Park","raw_affiliation_strings":["Department of Computer Science and Engineering, INHA University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, INHA University","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100676173","display_name":"Sang\u2010Chul Lee","orcid":"https://orcid.org/0000-0002-6973-2416"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Chul Lee","raw_affiliation_strings":["Department of Computer Science and Engineering, INHA University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, INHA University","institution_ids":["https://openalex.org/I191879574"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5109226983"],"corresponding_institution_ids":["https://openalex.org/I191879574"],"apc_list":null,"apc_paid":null,"fwci":0.579,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.80713727,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"19","issue":null,"first_page":"3844","last_page":"3848"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9990000128746033,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9986000061035156,"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/segmentation","display_name":"Segmentation","score":0.8052799701690674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6836696863174438},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.6463047862052917},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6191357970237732},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6040093898773193},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6038998961448669},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6018148064613342},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.5852354168891907},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.5369093418121338},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5021641254425049},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5012350082397461},{"id":"https://openalex.org/keywords/vagueness","display_name":"Vagueness","score":0.49216288328170776},{"id":"https://openalex.org/keywords/region-growing","display_name":"Region growing","score":0.43956315517425537},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4139552712440491},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32425254583358765},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09075239300727844}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8052799701690674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6836696863174438},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.6463047862052917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6191357970237732},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6040093898773193},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6038998961448669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6018148064613342},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5852354168891907},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.5369093418121338},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5021641254425049},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5012350082397461},{"id":"https://openalex.org/C2776825360","wikidata":"https://www.wikidata.org/wiki/Q1411921","display_name":"Vagueness","level":3,"score":0.49216288328170776},{"id":"https://openalex.org/C206824153","wikidata":"https://www.wikidata.org/wiki/Q1169834","display_name":"Region growing","level":5,"score":0.43956315517425537},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4139552712440491},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32425254583358765},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09075239300727844},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8297002","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.49000000953674316},{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W964272490","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1763493018","https://openalex.org/W1945243521","https://openalex.org/W1979393293","https://openalex.org/W2005325710","https://openalex.org/W2019062120","https://openalex.org/W2069533927","https://openalex.org/W2073057651","https://openalex.org/W2094821343","https://openalex.org/W2102255559","https://openalex.org/W2104095591","https://openalex.org/W2116040950","https://openalex.org/W2120601853","https://openalex.org/W2123269393","https://openalex.org/W2133059825","https://openalex.org/W2142332605","https://openalex.org/W2160041854","https://openalex.org/W2408588098","https://openalex.org/W2534622136","https://openalex.org/W2575706047","https://openalex.org/W3211330693","https://openalex.org/W4300189514","https://openalex.org/W6680096826","https://openalex.org/W6732253141","https://openalex.org/W6805924992"],"related_works":["https://openalex.org/W2356881624","https://openalex.org/W2394279717","https://openalex.org/W2364730859","https://openalex.org/W2359699469","https://openalex.org/W2386644571","https://openalex.org/W2619283684","https://openalex.org/W2383720950","https://openalex.org/W4281943322","https://openalex.org/W2948887454","https://openalex.org/W2551987074"],"abstract_inverted_index":{"Accurate":[0],"cell":[1,42,94],"segmentation":[2,56,63],"is":[3,96,125,151],"one":[4],"of":[5,24,28,34,53,57,65,81,84,113,115,123,129,138,174,177,179],"the":[6,79,82,93,111,116,139,146,149,154],"critical,":[7],"yet":[8],"challenging":[9],"problems":[10],"in":[11,172],"microscopy":[12],"images":[13],"due":[14],"to":[15,50],"ambiguous":[16],"boundaries":[17],"as":[18,20],"well":[19],"a":[21,32,62,88,143],"wide":[22],"variation":[23],"shapes":[25],"and":[26,73,132,183],"sizes":[27],"cells.":[29,75],"Even":[30],"though":[31],"number":[33,112],"existing":[35,170],"methods":[36,171],"have":[37],"achieved":[38],"decent":[39],"results":[40],"for":[41],"segmentation,":[43],"boundary":[44,184],"vagueness":[45],"between":[46,71],"adjoining":[47,85],"cells":[48,66,86,108],"tended":[49],"cause":[51],"generation":[52],"perceptually":[54],"inaccurate":[55],"stained":[58],"nuclei.":[59],"We":[60],"propose":[61],"method":[64],"based":[67],"on":[68,110],"structural":[69,104],"dissimilarity":[70,105],"embodied":[72,107],"imaged":[74],"From":[76],"assumption":[77],"that":[78],"shape":[80],"region":[83,95,122,178],"follows":[87],"2D":[89,117],"Gaussian":[90,118],"mixture":[91,119],"model,":[92],"divided":[97],"by":[98,127,153],"an":[99],"expectation-maximization":[100],"method.":[101],"The":[102,121],"lowest":[103],"using":[106],"decides":[109],"components":[114],"model.":[120],"interest":[124,180],"extracted":[126],"implementation":[128],"both":[130,175],"global":[131],"local":[133,140],"thresholdings,":[134],"which":[135],"performs":[136],"binarization":[137],"image":[141],"with":[142,167],"seed":[144,150],"at":[145],"center,":[147],"where":[148],"obtained":[152],"maximally":[155],"stable":[156],"extremal":[157],"regions.":[158],"Our":[159],"approach":[160],"presented":[161],"considerably":[162],"higher":[163],"evaluation":[164],"scores":[165],"compared":[166],"other":[168],"five":[169],"terms":[173],"accuracies":[176],"(ROI)":[181],"detection":[182],"discrimination.":[185]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}