{"id":"https://openalex.org/W2443115782","doi":"https://doi.org/10.1109/isda.2015.7489187","title":"Noise adaptive FCM algorithm for segmentation of MRI brain images using local and non-local spatial information","display_name":"Noise adaptive FCM algorithm for segmentation of MRI brain images using local and non-local spatial information","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2443115782","doi":"https://doi.org/10.1109/isda.2015.7489187","mag":"2443115782"},"language":"en","primary_location":{"id":"doi:10.1109/isda.2015.7489187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isda.2015.7489187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)","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/A5029407982","display_name":"Nitesh Arora","orcid":"https://orcid.org/0000-0003-2301-4264"},"institutions":[{"id":"https://openalex.org/I105094715","display_name":"National Institute of Technology Kurukshetra","ror":"https://ror.org/04909p852","country_code":"IN","type":"education","lineage":["https://openalex.org/I105094715"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Nitesh Arora","raw_affiliation_strings":["National Institute of Technology, Kurukshetra"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology, Kurukshetra","institution_ids":["https://openalex.org/I105094715"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011504283","display_name":"Rajoo Pandey","orcid":"https://orcid.org/0000-0002-2775-0448"},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajoo Pandey","raw_affiliation_strings":["National Institute of Technology Calicut, Calicut, Kerala, IN"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology Calicut, Calicut, Kerala, IN","institution_ids":["https://openalex.org/I114845381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5029407982"],"corresponding_institution_ids":["https://openalex.org/I105094715"],"apc_list":null,"apc_paid":null,"fwci":0.5523,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76767947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"12","issue":null,"first_page":"610","last_page":"617"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9991999864578247,"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":0.9991999864578247,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9983999729156494,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/noise","display_name":"Noise (video)","score":0.6267004013061523},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5826578140258789},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.570121705532074},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5591115951538086},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5511800050735474},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.542343258857727},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5249937772750854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5189852118492126},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5019166469573975},{"id":"https://openalex.org/keywords/median-filter","display_name":"Median filter","score":0.4162179231643677},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3743492364883423},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3361393213272095},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33131811022758484},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32622992992401123},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06883153319358826}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6267004013061523},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5826578140258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.570121705532074},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5591115951538086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5511800050735474},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.542343258857727},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5249937772750854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5189852118492126},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5019166469573975},{"id":"https://openalex.org/C55352655","wikidata":"https://www.wikidata.org/wiki/Q304247","display_name":"Median filter","level":4,"score":0.4162179231643677},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3743492364883423},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3361393213272095},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33131811022758484},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32622992992401123},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06883153319358826}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isda.2015.7489187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isda.2015.7489187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1589895371","https://openalex.org/W1990368529","https://openalex.org/W1992147426","https://openalex.org/W2012742622","https://openalex.org/W2038533573","https://openalex.org/W2056766567","https://openalex.org/W2083790562","https://openalex.org/W2097073572","https://openalex.org/W2108859253","https://openalex.org/W2109577576","https://openalex.org/W2113076747","https://openalex.org/W2115242586","https://openalex.org/W2118136617","https://openalex.org/W2138762030","https://openalex.org/W2165012164"],"related_works":["https://openalex.org/W2365412889","https://openalex.org/W996507331","https://openalex.org/W2366091560","https://openalex.org/W2368814124","https://openalex.org/W2154655659","https://openalex.org/W2547062837","https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2361233022","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Image":[0],"segmentation":[1,168,196],"is":[2,71,86,132,149,185,189,220,271,296],"an":[3],"important":[4],"task":[5],"in":[6,116,151,233],"many":[7,26],"image":[8,118,263,303],"processing":[9],"applications.":[10],"Fuzzy":[11],"C":[12],"means":[13],"clustering":[14],"algorithm":[15,33,46,177,188,194,241,270,290,295],"has":[16],"been":[17],"widely":[18],"used":[19],"for":[20,166,195],"the":[21,30,36,42,56,74,77,82,110,117,120,146,152,158,167,180,213,216,224,239,245,256,262,268,288,293,311],"segmentation.":[22],"There":[23],"are":[24],"so":[25],"extended":[27],"versions":[28],"of":[29,44,79,97,102,113,143,169,197,267,287,310],"traditional":[31],"FCM":[32,45,153,176,193],"which":[34,90,155,211],"utilizes":[35,243],"local":[37,122,138,159,162,202,226,246,249],"spatial":[38,69,139,163,205,218,227],"information":[39,70,123,164,184,219,250],"to":[40,47,54,64,92,299,307],"decrease":[41],"sensitivity":[43],"noise,":[48],"but":[49],"all":[50],"these":[51],"algorithms":[52],"fail":[53],"segment":[55],"images":[57,200],"heavily":[58],"contaminated":[59],"by":[60,125,273],"noise.":[61],"In":[62,172],"order":[63],"overcome":[65],"this":[66,127,173],"problem,":[67],"non-local":[68,80,183,204],"extracted":[72],"from":[73],"image.":[75],"During":[76],"extraction":[78],"information,":[81,228],"filtering":[83,129,147,236],"parameter":[84,89,130,148,210],"h":[85,131],"a":[87,99],"crucial":[88],"needs":[91],"be":[93,106],"appropriately":[94],"determined.":[95],"Instead":[96],"using":[98,109,126,179,201,223],"constant":[100],"value":[101],"h,":[103],"it":[104,230,252],"can":[105],"adaptively":[107,144,242],"determined":[108],"standard":[111],"deviation":[112],"noise":[114,135,181,257,300],"present":[115],"and":[119,160,203,247,301],"non":[121,137,161,225,248],"calculated":[124,222],"adaptive":[128,136,182,192,232],"termed":[133],"as":[134,258,260,278,280,305],"information.":[140,206],"This":[141,187],"property":[142],"determining":[145],"utilized":[150],"algorithm,":[154],"uses":[156],"both":[157,244],"(MFCM)":[165,178],"MRI":[170,198,283],"images.":[171,284],"paper":[174],"Modified":[175],"proposed.":[186],"called":[190],"Noise":[191],"brain":[199,282],"The":[207,265,285],"trade":[208],"off":[209],"controls":[212],"trade-off":[214],"between":[215],"two":[217],"also":[221,231],"making":[229,251],"accordance":[234],"with":[235],"parameter.":[237],"Therefore":[238],"proposed":[240,269,289,294],"more":[253],"robust":[254,298],"against":[255],"well":[259,279],"preserving":[261],"details.":[264],"efficiency":[266],"demonstrated":[272],"validation":[274],"studies":[275],"on":[276],"synthetic":[277],"simulated":[281],"results":[286],"show":[291],"that":[292],"very":[297],"other":[302,308],"artifacts":[304],"compared":[306],"state":[309],"art":[312],"algorithms.":[313]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}