{"id":"https://openalex.org/W2119213432","doi":"https://doi.org/10.1109/icassp.2011.5946501","title":"Contour-based hidden Markov model to segment 2D ultrasound images","display_name":"Contour-based hidden Markov model to segment 2D ultrasound images","publication_year":2011,"publication_date":"2011-05-01","ids":{"openalex":"https://openalex.org/W2119213432","doi":"https://doi.org/10.1109/icassp.2011.5946501","mag":"2119213432"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2011.5946501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2011.5946501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5073946580","display_name":"Xiaoning Qian","orcid":"https://orcid.org/0000-0002-4347-2476"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaoning Qian","raw_affiliation_strings":["Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA","University of South Florida, Department of Computer Science & Engineering, Tampa, 33620, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]},{"raw_affiliation_string":"University of South Florida, Department of Computer Science & Engineering, Tampa, 33620, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081620315","display_name":"Byung-Jun Yoon","orcid":"https://orcid.org/0000-0001-9328-1101"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Byung-Jun Yoon","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Texas A and M University, College Station, TX, USA","Texas A&M University, Department of Electrical & Computer Engineering, College Station 77843, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Texas A and M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"Texas A&M University, Department of Electrical & Computer Engineering, College Station 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5073946580"],"corresponding_institution_ids":["https://openalex.org/I2613432"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.16240567,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"705","last_page":"708"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.994700014591217,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7572234869003296},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7040717005729675},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6588735580444336},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6389214992523193},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6333781480789185},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6106579899787903},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5941056609153748},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5656057000160217},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5229576230049133},{"id":"https://openalex.org/keywords/active-contour-model","display_name":"Active contour model","score":0.47143760323524475},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.4596502482891083},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.3984958231449127},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17824307084083557}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7572234869003296},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7040717005729675},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6588735580444336},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6389214992523193},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6333781480789185},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6106579899787903},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5941056609153748},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5656057000160217},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5229576230049133},{"id":"https://openalex.org/C112353826","wikidata":"https://www.wikidata.org/wiki/Q127313","display_name":"Active contour model","level":4,"score":0.47143760323524475},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.4596502482891083},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.3984958231449127},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17824307084083557},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2011.5946501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2011.5946501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.722.431","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.722.431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.usf.edu/%7Exqian/ICASSP_2011.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W187962970","https://openalex.org/W1485462945","https://openalex.org/W1543363024","https://openalex.org/W1677409904","https://openalex.org/W1971341890","https://openalex.org/W1999731065","https://openalex.org/W2057231415","https://openalex.org/W2104563273","https://openalex.org/W2109292231","https://openalex.org/W2111250300","https://openalex.org/W2112713547","https://openalex.org/W2119249988","https://openalex.org/W2119605622","https://openalex.org/W2119823327","https://openalex.org/W2120023383","https://openalex.org/W2123139184","https://openalex.org/W2130470481","https://openalex.org/W2151103935","https://openalex.org/W2167578079","https://openalex.org/W2167825066","https://openalex.org/W3144434669","https://openalex.org/W6629098971","https://openalex.org/W6632582182","https://openalex.org/W6664798186","https://openalex.org/W6675541728","https://openalex.org/W6679048381"],"related_works":["https://openalex.org/W2065648684","https://openalex.org/W2009383287","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2055824452","https://openalex.org/W2121688719","https://openalex.org/W2727313114","https://openalex.org/W2016481886","https://openalex.org/W4241911733"],"abstract_inverted_index":{"The":[0,108,135],"segmentation":[1,30,64,87],"of":[2,11,14,80,97,148],"ultrasound":[3,35,132],"images":[4,36,54,133],"is":[5,113],"challenging":[6],"due":[7],"to":[8,76,125],"the":[9,46,63,77,95,117,127,146],"difficulty":[10],"appropriate":[12],"modeling":[13,100],"their":[15],"appearance":[16,99],"variations":[17],"including":[18],"speckle":[19],"as":[20,22],"well":[21],"signal":[23],"dropout.":[24],"We":[25,121],"propose":[26],"a":[27],"novel":[28],"automatic":[29],"method":[31,88,124,136],"for":[32,110,141],"2D":[33],"cardiac":[34,131],"based":[37],"on":[38],"hidden":[39],"Markov":[40],"models":[41],"(HMMs).":[42],"By":[43],"directly":[44],"exploiting":[45],"local":[47,81],"image":[48,143,149],"characteristics":[49],"around":[50],"contour":[51,111],"points":[52],"in":[53,83,101,129],"and":[55,115],"integrating":[56],"them":[57],"into":[58],"contour-based":[59],"HMMs,":[60,85],"we":[61],"solve":[62],"problem":[65],"by":[66],"graph":[67],"matching":[68],"using":[69],"an":[70],"efficient":[71],"dynamic":[72],"programming":[73],"algorithm.":[74],"Due":[75],"direct":[78],"integration":[79],"properties":[82],"our":[84,86,123],"automatically":[89],"deals":[90],"with":[91,145],"inhomogeneity":[92],"but":[93],"avoids":[94],"complexities":[96],"explicit":[98],"classical":[102],"Maximum":[103],"A":[104],"Posteriori":[105],"(MAP)":[106],"approaches.":[107],"optimization":[109],"extraction":[112],"straightforward":[114],"guarantees":[116],"global":[118],"optimal":[119],"results.":[120],"implemented":[122],"segment":[126],"endocardium":[128],"short-axis":[130],"successfully.":[134],"can":[137],"also":[138],"be":[139],"used":[140],"other":[142],"modalities":[144],"presence":[147],"inhomogeneity.":[150]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
