{"id":"https://openalex.org/W2943904649","doi":"https://doi.org/10.1109/ipas.2018.8708865","title":"Discriminant Textural Feature Selection and Classification for a Computerized Fetal Hydrocephalus Detection","display_name":"Discriminant Textural Feature Selection and Classification for a Computerized Fetal Hydrocephalus Detection","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2943904649","doi":"https://doi.org/10.1109/ipas.2018.8708865","mag":"2943904649"},"language":"en","primary_location":{"id":"doi:10.1109/ipas.2018.8708865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipas.2018.8708865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS)","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/A5052971904","display_name":"Hanene Sahli","orcid":"https://orcid.org/0000-0002-8253-9805"},"institutions":[{"id":"https://openalex.org/I108714496","display_name":"Tunis University","ror":"https://ror.org/02q1spa57","country_code":"TN","type":"education","lineage":["https://openalex.org/I108714496"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Hanene Sahli","raw_affiliation_strings":["ENSIT, Universit\u00e9 de Tunis, Tunisia"],"affiliations":[{"raw_affiliation_string":"ENSIT, Universit\u00e9 de Tunis, Tunisia","institution_ids":["https://openalex.org/I108714496"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073163982","display_name":"Aymen Mouelhi","orcid":"https://orcid.org/0000-0002-6642-2594"},"institutions":[{"id":"https://openalex.org/I108714496","display_name":"Tunis University","ror":"https://ror.org/02q1spa57","country_code":"TN","type":"education","lineage":["https://openalex.org/I108714496"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Aymen Mouelhi","raw_affiliation_strings":["ENSIT, Universit\u00e9 de Tunis, Tunisia"],"affiliations":[{"raw_affiliation_string":"ENSIT, Universit\u00e9 de Tunis, Tunisia","institution_ids":["https://openalex.org/I108714496"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007949063","display_name":"Mounir Sayadi","orcid":"https://orcid.org/0000-0003-4270-421X"},"institutions":[{"id":"https://openalex.org/I108714496","display_name":"Tunis University","ror":"https://ror.org/02q1spa57","country_code":"TN","type":"education","lineage":["https://openalex.org/I108714496"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Mounir Sayadi","raw_affiliation_strings":["ENSIT, Universit\u00e9 de Tunis, Tunisia"],"affiliations":[{"raw_affiliation_string":"ENSIT, Universit\u00e9 de Tunis, Tunisia","institution_ids":["https://openalex.org/I108714496"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063156029","display_name":"R. Rachdi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155821","display_name":"Military Hospital of Tunis","ror":"https://ror.org/04n4f3r80","country_code":"TN","type":"healthcare","lineage":["https://openalex.org/I4210155821"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Radhouane Rachdi","raw_affiliation_strings":["Department of maternity, Military Hospital, Tunis, Tunisia"],"affiliations":[{"raw_affiliation_string":"Department of maternity, Military Hospital, Tunis, Tunisia","institution_ids":["https://openalex.org/I4210155821"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5052971904"],"corresponding_institution_ids":["https://openalex.org/I108714496"],"apc_list":null,"apc_paid":null,"fwci":0.3644,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72789479,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11184","display_name":"Neonatal and fetal brain pathology","score":0.9689000248908997,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9387999773025513,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hydrocephalus","display_name":"Hydrocephalus","score":0.747968316078186},{"id":"https://openalex.org/keywords/abnormality","display_name":"Abnormality","score":0.6403130292892456},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6380696296691895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6373282074928284},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6288357377052307},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6108105778694153},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6094022393226624},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5795347094535828},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5084856748580933},{"id":"https://openalex.org/keywords/fetus","display_name":"Fetus","score":0.480579137802124},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4716797471046448},{"id":"https://openalex.org/keywords/fetal-head","display_name":"Fetal head","score":0.4615905284881592},{"id":"https://openalex.org/keywords/classification-scheme","display_name":"Classification scheme","score":0.41231730580329895},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3607594966888428},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2605963349342346},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24185049533843994},{"id":"https://openalex.org/keywords/pregnancy","display_name":"Pregnancy","score":0.1279411017894745},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.07137855887413025}],"concepts":[{"id":"https://openalex.org/C2778134817","wikidata":"https://www.wikidata.org/wiki/Q193003","display_name":"Hydrocephalus","level":2,"score":0.747968316078186},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.6403130292892456},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6380696296691895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6373282074928284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6288357377052307},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6108105778694153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6094022393226624},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5795347094535828},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5084856748580933},{"id":"https://openalex.org/C172680121","wikidata":"https://www.wikidata.org/wiki/Q26513","display_name":"Fetus","level":3,"score":0.480579137802124},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4716797471046448},{"id":"https://openalex.org/C2779811377","wikidata":"https://www.wikidata.org/wiki/Q5445900","display_name":"Fetal head","level":4,"score":0.4615905284881592},{"id":"https://openalex.org/C13460635","wikidata":"https://www.wikidata.org/wiki/Q85753676","display_name":"Classification scheme","level":2,"score":0.41231730580329895},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3607594966888428},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2605963349342346},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24185049533843994},{"id":"https://openalex.org/C2779234561","wikidata":"https://www.wikidata.org/wiki/Q11995","display_name":"Pregnancy","level":2,"score":0.1279411017894745},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07137855887413025},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipas.2018.8708865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipas.2018.8708865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2072681792","https://openalex.org/W2075514126","https://openalex.org/W2131917744","https://openalex.org/W2168822372","https://openalex.org/W2295330322","https://openalex.org/W2401539061","https://openalex.org/W2603928600","https://openalex.org/W2739065277","https://openalex.org/W2786180624","https://openalex.org/W2789879424","https://openalex.org/W2800378670","https://openalex.org/W2837061503","https://openalex.org/W2887591740","https://openalex.org/W4244195598","https://openalex.org/W6749045084"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W2362114017","https://openalex.org/W1999647744","https://openalex.org/W3147024994","https://openalex.org/W1978302214","https://openalex.org/W2374055396","https://openalex.org/W2063246903","https://openalex.org/W2021817983","https://openalex.org/W3021047493","https://openalex.org/W2371177901"],"abstract_inverted_index":{"This":[0],"work":[1,84],"presents":[2],"an":[3],"improved":[4],"procedure":[5],"able":[6],"to":[7,20,103,132,152],"achieve":[8],"straight":[9],"classification":[10,145],"of":[11,24,34,43,51,76,82,88,93,126,136],"fetal":[12,27,53,67,94,120],"abnormality":[13],"in":[14,18,101,156],"head":[15,68],"ultrasound":[16],"images":[17,122],"order":[19,102],"supply":[21],"quantitative":[22],"assessment":[23],"healthy":[25],"or":[26],"hydrocephalus":[28,95,144],"(H-/H+)":[29],"cases.":[30],"Indeed,":[31],"the":[32,41,48,61,74,86,109,124,127,133],"majority":[33],"physician":[35],"rely":[36],"on":[37,73,119],"manual":[38,134],"diagnostic":[39],"by":[40],"use":[42],"morphological":[44],"characteristics":[45],"before":[46],"interpreting":[47],"clinical":[49],"implication":[50],"all":[52],"region":[54],"measurements.":[55],"The":[56,79,139],"proposed":[57,128,140],"method":[58,129],"deals":[59],"with":[60],"discriminant":[62],"textural":[63,99],"features":[64,100],"extraction":[65],"from":[66],"dataset":[69],"that":[70],"can":[71],"contribute":[72],"recognition":[75],"cerebral":[77],"diseases.":[78],"main":[80],"contribution":[81],"this":[83,154],"is":[85],"proposal":[87],"a":[89,113,149],"fully":[90],"computerized":[91],"approach":[92],"detection":[96],"using":[97],"relevant":[98],"study":[104],"its":[105],"aptitude":[106],"for":[107],"evaluating":[108],"abnormal":[110],"subjects":[111],"within":[112],"reduced":[114],"processing":[115],"time.":[116],"Experimental":[117],"results":[118],"US":[121],"show":[123,148],"efficiency":[125],"when":[130],"compared":[131],"delineations":[135],"experts'":[137],"evaluation.":[138],"scheme":[141],"provides":[142],"suitable":[143],"rates":[146],"and":[147],"good":[150],"ability":[151],"distinguish":[153],"anomaly":[155],"early":[157],"stage.":[158]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
