{"id":"https://openalex.org/W2885119478","doi":"https://doi.org/10.1109/iwssip.2018.8439679","title":"Rigid Registration Method for Medical Volumes with Large Deformations and Missing Data","display_name":"Rigid Registration Method for Medical Volumes with Large Deformations and Missing Data","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2885119478","doi":"https://doi.org/10.1109/iwssip.2018.8439679","mag":"2885119478"},"language":"en","primary_location":{"id":"doi:10.1109/iwssip.2018.8439679","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwssip.2018.8439679","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP)","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/A5015739274","display_name":"Marek Wodzi\u0144ski","orcid":"https://orcid.org/0000-0002-8076-6246"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Marek Wodzinski","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland"],"affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073000204","display_name":"Andrzej Skalski","orcid":"https://orcid.org/0000-0003-2299-458X"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Andrzej Skalski","raw_affiliation_strings":["Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland"],"affiliations":[{"raw_affiliation_string":"Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland","institution_ids":["https://openalex.org/I686019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5015739274"],"corresponding_institution_ids":["https://openalex.org/I686019"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07387453,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"110","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":0.9993000030517578,"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":0.9993000030517578,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9979000091552734,"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":0.9955999851226807,"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/maxima-and-minima","display_name":"Maxima and minima","score":0.8019620180130005},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.6949667930603027},{"id":"https://openalex.org/keywords/iterative-closest-point","display_name":"Iterative closest point","score":0.6835461258888245},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.631496012210846},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6087941527366638},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5976434350013733},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5810774564743042},{"id":"https://openalex.org/keywords/point-set-registration","display_name":"Point set registration","score":0.5480797290802002},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5279763340950012},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4951125681400299},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4670630395412445},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45311862230300903},{"id":"https://openalex.org/keywords/deformation","display_name":"Deformation (meteorology)","score":0.4290221333503723},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41429612040519714},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4116034507751465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3918887972831726},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3618486523628235},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30749914050102234},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2840873599052429},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.1785174310207367},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.10436421632766724}],"concepts":[{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.8019620180130005},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.6949667930603027},{"id":"https://openalex.org/C195958017","wikidata":"https://www.wikidata.org/wiki/Q1675268","display_name":"Iterative closest point","level":3,"score":0.6835461258888245},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.631496012210846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6087941527366638},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5976434350013733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5810774564743042},{"id":"https://openalex.org/C200336642","wikidata":"https://www.wikidata.org/wiki/Q15058706","display_name":"Point set registration","level":3,"score":0.5480797290802002},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5279763340950012},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4951125681400299},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4670630395412445},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45311862230300903},{"id":"https://openalex.org/C204366326","wikidata":"https://www.wikidata.org/wiki/Q3027650","display_name":"Deformation (meteorology)","level":2,"score":0.4290221333503723},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41429612040519714},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4116034507751465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3918887972831726},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3618486523628235},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30749914050102234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2840873599052429},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.1785174310207367},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.10436421632766724},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"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:10.1109/iwssip.2018.8439679","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwssip.2018.8439679","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1493695579","https://openalex.org/W1992483663","https://openalex.org/W2015471391","https://openalex.org/W2019346246","https://openalex.org/W2025287381","https://openalex.org/W2026620973","https://openalex.org/W2049981393","https://openalex.org/W2065319605","https://openalex.org/W2071881327","https://openalex.org/W2082595705","https://openalex.org/W2085261163","https://openalex.org/W2106199912","https://openalex.org/W2107987248","https://openalex.org/W2113576511","https://openalex.org/W2115167851","https://openalex.org/W2117271072","https://openalex.org/W2119605622","https://openalex.org/W2119851068","https://openalex.org/W2126060993","https://openalex.org/W2136503713","https://openalex.org/W2140139548","https://openalex.org/W2151103935","https://openalex.org/W2165428671","https://openalex.org/W2177274842","https://openalex.org/W2514120346","https://openalex.org/W2620749649","https://openalex.org/W3139835851","https://openalex.org/W6683751579"],"related_works":["https://openalex.org/W2908649049","https://openalex.org/W1994704147","https://openalex.org/W2016471627","https://openalex.org/W4214932852","https://openalex.org/W2032203265","https://openalex.org/W2037143534","https://openalex.org/W2888631859","https://openalex.org/W2102629623","https://openalex.org/W2083904196","https://openalex.org/W3154360165"],"abstract_inverted_index":{"A":[0],"rigid":[1,20],"registration":[2,21,64],"is":[3,33,52,104],"a":[4,9],"crucial":[5],"initial":[6],"step":[7],"for":[8,82],"correct":[10,80],"deformable":[11],"medical":[12],"image":[13],"registration.":[14],"In":[15],"this":[16],"work,":[17],"we":[18],"propose":[19],"method":[22,32,51],"resistant":[23],"to":[24,54,122],"large":[25],"deformations":[26,81],"and":[27,41,62,78,90,108,120],"missing":[28],"data.":[29],"The":[30,50,69,102],"proposed":[31,70],"based":[34],"on":[35],"the":[36,58],"bones":[37],"segmentation,":[38],"feature":[39],"matching":[40],"outliers":[42],"elimination":[43],"inspired":[44],"by":[45],"traditional":[46],"computer":[47],"vision":[48],"approach.":[49],"compared":[53],"other":[55],"state-of-the-art":[56],"algorithms,":[57],"iterative":[59],"closest":[60],"point":[61],"intensity-based":[63],"using":[65,106],"widely":[66],"available":[67],"dataset.":[68],"algorithm":[71,103],"does":[72],"not":[73],"fail":[74,123],"into":[75,124],"local":[76,125],"minima":[77],"reconstructs":[79],"average":[83],"vector":[84],"length":[85],"greater":[86],"than":[87,95],"150":[88],"mm":[89],"data":[91],"overlap":[92],"ratio":[93],"less":[94],"50%,":[96],"where":[97],"currently":[98],"applied":[99],"methods":[100],"fail.":[101],"evaluated":[105],"angle":[107],"magnitude":[109],"errors":[110],"between":[111,117],"corresponding":[112],"deformation":[113],"vectors,":[114],"Hausdorff":[115],"distance":[116],"bone":[118],"segmentations":[119],"resistance":[121],"minima.":[126]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}