{"id":"https://openalex.org/W2758209205","doi":"https://doi.org/10.1109/mmar.2017.8046941","title":"Novel method for joining missing line fragments for medical image analysis","display_name":"Novel method for joining missing line fragments for medical image analysis","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2758209205","doi":"https://doi.org/10.1109/mmar.2017.8046941","mag":"2758209205"},"language":"en","primary_location":{"id":"doi:10.1109/mmar.2017.8046941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmar.2017.8046941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5015969182","display_name":"Patryk Najgebauer","orcid":"https://orcid.org/0000-0002-7168-3019"},"institutions":[{"id":"https://openalex.org/I130294970","display_name":"Cz\u0119stochowa University of Technology","ror":"https://ror.org/046awyn59","country_code":"PL","type":"education","lineage":["https://openalex.org/I130294970"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Patryk Najgebauer","raw_affiliation_strings":["Computer Vision and Data Mining Lab, Czestochowa University of Technology, Czestochowa, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision and Data Mining Lab, Czestochowa University of Technology, Czestochowa, Poland","institution_ids":["https://openalex.org/I130294970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043134796","display_name":"Leszek Rutkowski","orcid":"https://orcid.org/0000-0001-6960-9525"},"institutions":[{"id":"https://openalex.org/I130294970","display_name":"Cz\u0119stochowa University of Technology","ror":"https://ror.org/046awyn59","country_code":"PL","type":"education","lineage":["https://openalex.org/I130294970"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Leszek Rutkowski","raw_affiliation_strings":["Computer Vision and Data Mining Lab, Czestochowa University of Technology, Czestochowa, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision and Data Mining Lab, Czestochowa University of Technology, Czestochowa, Poland","institution_ids":["https://openalex.org/I130294970"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036573895","display_name":"Rafa\u0142 Scherer","orcid":"https://orcid.org/0000-0001-9592-262X"},"institutions":[{"id":"https://openalex.org/I130294970","display_name":"Cz\u0119stochowa University of Technology","ror":"https://ror.org/046awyn59","country_code":"PL","type":"education","lineage":["https://openalex.org/I130294970"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Rafal Scherer","raw_affiliation_strings":["Computer Vision and Data Mining Lab, Czestochowa University of Technology, Czestochowa, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision and Data Mining Lab, Czestochowa University of Technology, Czestochowa, Poland","institution_ids":["https://openalex.org/I130294970"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130294970"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"861","last_page":"866"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9986000061035156,"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":0.9952999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7225514650344849},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6773960590362549},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.59194415807724},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.5567820072174072},{"id":"https://openalex.org/keywords/magnification","display_name":"Magnification","score":0.5302091836929321},{"id":"https://openalex.org/keywords/line-segment","display_name":"Line segment","score":0.49455997347831726},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.49124497175216675},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4560302793979645},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.43876007199287415},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4285638630390167},{"id":"https://openalex.org/keywords/image-gradient","display_name":"Image gradient","score":0.42304527759552},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35239177942276},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34799206256866455},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.26659905910491943},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2655485272407532},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.1078934371471405}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7225514650344849},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6773960590362549},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.59194415807724},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.5567820072174072},{"id":"https://openalex.org/C4144372","wikidata":"https://www.wikidata.org/wiki/Q675287","display_name":"Magnification","level":2,"score":0.5302091836929321},{"id":"https://openalex.org/C182124507","wikidata":"https://www.wikidata.org/wiki/Q166154","display_name":"Line segment","level":2,"score":0.49455997347831726},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.49124497175216675},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4560302793979645},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.43876007199287415},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4285638630390167},{"id":"https://openalex.org/C182037307","wikidata":"https://www.wikidata.org/wiki/Q17039097","display_name":"Image gradient","level":5,"score":0.42304527759552},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35239177942276},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34799206256866455},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26659905910491943},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2655485272407532},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.1078934371471405}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmar.2017.8046941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmar.2017.8046941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6800000071525574,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W8437397","https://openalex.org/W22040386","https://openalex.org/W33747781","https://openalex.org/W1677409904","https://openalex.org/W1970554717","https://openalex.org/W1992752075","https://openalex.org/W1996655877","https://openalex.org/W2003370853","https://openalex.org/W2006982476","https://openalex.org/W2045899586","https://openalex.org/W2056798569","https://openalex.org/W2064543782","https://openalex.org/W2093595491","https://openalex.org/W2102512156","https://openalex.org/W2108729336","https://openalex.org/W2116504721","https://openalex.org/W2117193984","https://openalex.org/W2119588908","https://openalex.org/W2119823327","https://openalex.org/W2132031490","https://openalex.org/W2133267925","https://openalex.org/W2139577851","https://openalex.org/W2140927567","https://openalex.org/W2143668817","https://openalex.org/W2145023731","https://openalex.org/W2151049637","https://openalex.org/W2157898655","https://openalex.org/W2166760871","https://openalex.org/W2168014483","https://openalex.org/W2171229091","https://openalex.org/W2288895205","https://openalex.org/W2497426408","https://openalex.org/W2508190764","https://openalex.org/W6600324250","https://openalex.org/W6676449312","https://openalex.org/W6679372278","https://openalex.org/W6682856636","https://openalex.org/W6684905197"],"related_works":["https://openalex.org/W2739273756","https://openalex.org/W2387510934","https://openalex.org/W4312567938","https://openalex.org/W4283806690","https://openalex.org/W4281937456","https://openalex.org/W2545065926","https://openalex.org/W2356242818","https://openalex.org/W2348584852","https://openalex.org/W2545393398","https://openalex.org/W2116510815"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,74,101,215],"method":[3,16,87,109,163,219],"of":[4,10,31,52,73,80,84,124,130,152,159,167,174,179,187,197,204,214,229,236],"predictive":[5],"reconstructing":[6],"connections":[7],"between":[8],"parts":[9,151],"object":[11,95],"outlines":[12,206],"in":[13,35,100,211,233],"images.":[14,32],"The":[15,86,104,118],"was":[17],"developed":[18],"mainly":[19],"to":[20,28,66,93,111,121,142,193,223],"analyze":[21],"microscopic":[22],"medical":[23],"images":[24,37],"but":[25],"is":[26,62,88],"applicable":[27],"other":[29,150],"types":[30],"Examined":[33],"objects":[34,43,54],"such":[36,53],"are":[38,207],"highly":[39],"transparent,":[40],"moreover":[41],"close":[42],"can":[44,55],"overlap":[45],"each":[46],"other.":[47],"Thus,":[48,217],"segmentation":[49],"and":[50,97,127,139,184,199],"separation":[51],"be":[56],"difficult.":[57],"Another":[58],"frequently":[59],"occurring":[60],"problem":[61],"partial":[63],"blur":[64],"due":[65],"high":[67],"image":[68,82],"magnification.":[69],"Large":[70],"focal":[71],"length":[72],"microscope":[75],"dramatically":[76],"narrows":[77],"the":[78,107,112,122,128,133,135,144,149,153,157,162,165,168,172,175,177,180,185,195,201,212,218,224,227,230,234],"range":[79],"sharp":[81],"(depth":[83],"field).":[85],"based":[89],"on":[90,226],"edge":[91,181],"detection":[92],"extract":[94],"contours":[96],"represent":[98],"them":[99],"vector":[102],"form.":[103],"logic":[105],"behind":[106],"presented":[108],"refers":[110],"Gestalt":[113],"Laws":[114],"describing":[115],"human":[116],"perception.":[117],"method,":[119,202],"according":[120],"law":[123],"good":[125],"continuation":[126],"principle":[129],"similarity,":[131],"evaluates":[132],"neighborhood":[134],"interrupted":[136],"contour":[137,160],"path,":[138],"then":[140],"tries":[141],"determine":[143],"most":[145],"appropriate":[146],"connection":[147],"with":[148],"contour.":[154],"To":[155],"assess":[156],"similarity":[158],"parts,":[161],"examines":[164],"orientation":[166],"line":[169],"determined":[170],"by":[171,209],"gradient":[173],"edge,":[176],"characteristics":[178],"cross":[182],"section":[183],"direction":[186],"its":[188],"current":[189],"course.":[190],"In":[191],"order":[192],"reduce":[194],"amount":[196],"data":[198],"accelerate":[200],"fragments":[203],"detected":[205],"represented":[208],"vectors":[210],"form":[213],"graph.":[216],"has":[220],"faster":[221],"access":[222],"information":[225],"course":[228],"edges":[231],"than":[232],"case":[235],"bitmap-based":[237],"representations.":[238]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}