{"id":"https://openalex.org/W3000896980","doi":"https://doi.org/10.36548/jiip.2019.2.004","title":"BIOMEDICAL IMAGE ANALYSIS USING SEMANTIC SEGMENTATION","display_name":"BIOMEDICAL IMAGE ANALYSIS USING SEMANTIC SEGMENTATION","publication_year":2019,"publication_date":"2019-12-31","ids":{"openalex":"https://openalex.org/W3000896980","doi":"https://doi.org/10.36548/jiip.2019.2.004","mag":"3000896980"},"language":"en","primary_location":{"id":"doi:10.36548/jiip.2019.2.004","doi":"https://doi.org/10.36548/jiip.2019.2.004","is_oa":true,"landing_page_url":"https://doi.org/10.36548/jiip.2019.2.004","pdf_url":null,"source":{"id":"https://openalex.org/S4210207215","display_name":"Journal of Innovative Image Processing","issn_l":"2582-4252","issn":["2582-4252"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Innovative Image Processing","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.36548/jiip.2019.2.004","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043223720","display_name":"V. Bindhu","orcid":"https://orcid.org/0000-0001-6507-7138","observed_orcids":["https://orcid.org/0000-0001-6507-7138"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bindhu V","raw_affiliation_strings":["Professor and Head, Department of Electronics and Communication Engineering, PPG Institute of Technology, Villankurichi Saravanampatti P.O, Coimbatore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor and Head, Department of Electronics and Communication Engineering, PPG Institute of Technology, Villankurichi Saravanampatti P.O, Coimbatore, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5043223720"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.2691,"has_fulltext":false,"cited_by_count":66,"citation_normalized_percentile":{"value":0.97520764,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"1","issue":"2","first_page":"91","last_page":"101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9951000213623047,"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":0.9951000213623047,"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":0.00279999990016222,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/semantic-segmentation","display_name":"semantic segmentation","score":0.9959999918937683},{"id":"https://openalex.org/keywords/medical-image-analysis","display_name":"medical image analysis","score":0.8460000157356262},{"id":"https://openalex.org/keywords/encoder-decoder-architecture","display_name":"encoder-decoder architecture","score":0.8040000200271606},{"id":"https://openalex.org/keywords/cascaded-convolutional-neural-network","display_name":"cascaded convolutional neural network","score":0.8349999785423279},{"id":"https://openalex.org/keywords/pixel-based-classification","display_name":"pixel classification","score":0.5320000052452087},{"id":"https://openalex.org/keywords/spatial-resolution-preservation","display_name":"spatial resolution preservation","score":0.7450000047683716},{"id":"https://openalex.org/keywords/medical-image-segmentation","display_name":"medical image segmentation","score":0.6179999709129333},{"id":"https://openalex.org/keywords/radiology","display_name":"radiology","score":0.4440000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7973979711532593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7882857322692871},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7767021656036377},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5749994516372681},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5734708309173584},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5407114028930664},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.5352846384048462},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5181995034217834},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5134371519088745},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4976063072681427},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4944636821746826},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4776117503643036},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.470001757144928},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4606632590293884},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.36548/jiip.2019.2.004","doi":"https://doi.org/10.36548/jiip.2019.2.004","is_oa":true,"landing_page_url":"https://doi.org/10.36548/jiip.2019.2.004","pdf_url":null,"source":{"id":"https://openalex.org/S4210207215","display_name":"Journal of Innovative Image Processing","issn_l":"2582-4252","issn":["2582-4252"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Innovative Image Processing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.36548/jiip.2019.2.004","doi":"https://doi.org/10.36548/jiip.2019.2.004","is_oa":true,"landing_page_url":"https://doi.org/10.36548/jiip.2019.2.004","pdf_url":null,"source":{"id":"https://openalex.org/S4210207215","display_name":"Journal of Innovative Image Processing","issn_l":"2582-4252","issn":["2582-4252"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Innovative Image Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.4043000042438507}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1573386842","https://openalex.org/W1901129140","https://openalex.org/W2177341437","https://openalex.org/W2432481613","https://openalex.org/W2517954747","https://openalex.org/W2526009326","https://openalex.org/W2735553491","https://openalex.org/W2753924563","https://openalex.org/W2762006829","https://openalex.org/W2792767783","https://openalex.org/W2980226421","https://openalex.org/W2980578413","https://openalex.org/W2981127131","https://openalex.org/W2984716259","https://openalex.org/W2990522736","https://openalex.org/W2990762651","https://openalex.org/W2994580779","https://openalex.org/W2995921265","https://openalex.org/W2996700021","https://openalex.org/W3001250425","https://openalex.org/W3001768830"],"related_works":["https://openalex.org/W4313640889","https://openalex.org/W2963609467","https://openalex.org/W3093884376","https://openalex.org/W3214171258","https://openalex.org/W4404628854","https://openalex.org/W7125183851","https://openalex.org/W4303649515","https://openalex.org/W1982611458","https://openalex.org/W4362499627","https://openalex.org/W4412810376"],"abstract_inverted_index":{"Semantic":[0],"Segmentation":[1],"is":[2],"a":[3,73,95,113],"very":[4,80],"active":[5],"area":[6],"of":[7,41,67,99,126,146],"research":[8],"in":[9,17,150,155],"the":[10,12,18,24,28,32,39,42,48,53,58,64,68,85,90,100,104,124,135,139,144,147,153,156,160],"examining":[11],"medical":[13],"images.":[14,43],"The":[15,44,107,129],"failure":[16],"conventional":[19],"segmentation":[20,50,97,137],"methods":[21,36],"to":[22,31,37,61,93,115],"preserve":[23],"full":[25],"resolution":[26,40,66,75],"throughout":[27],"network":[29,122],"led":[30],"research's":[33],"that":[34,78],"developed":[35],"protect":[38],"proposed":[45],"method":[46],"involves":[47],"semantic":[49,136],"model":[51],"for":[52,123],"biomedical":[54,101,131],"images":[55,102],"by":[56,143,159],"utilizing":[57],"encoder/decoder":[59],"structure":[60],"down":[62],"sample":[63],"spatial":[65],"input":[69],"data":[70],"and":[71,87,152],"develop":[72],"lower":[74],"feature":[76],"mapping":[77],"are":[79],"effective":[81],"at":[82],"distinguishing":[83],"between":[84],"classes":[86],"then":[88],"perform":[89],"up":[91],"samples":[92],"have":[94],"full-resolution":[96],"map":[98],"reducing":[103],"diagnostic":[105],"time.":[106],"frame":[108],"work":[109],"put":[110],"forth":[111],"utilizes":[112],"pixel":[114,116],"fully":[117],"trained":[118],"cascaded":[119],"convolutional":[120],"neural":[121],"task":[125],"image":[127,132],"segmentation.":[128],"evaluation":[130],"analysis":[133,157],"using":[134],"shows":[138],"performance":[140],"improvement":[141],"achieved":[142],"minimization":[145],"time":[148],"required":[149],"testing":[151],"augmentation":[154],"performed":[158],"radiologist.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":40}],"updated_date":"2026-10-21T09:56:26.102985","created_date":"2020-01-30T00:00:00"}
