{"id":"https://openalex.org/W2082323194","doi":"https://doi.org/10.1142/s0219467813500071","title":"REGION-BASED CONTRAST ENHANCEMENT OF DIGITAL MAMMOGRAMS USING AN IMPROVED WATERSHED SEGMENTATION","display_name":"REGION-BASED CONTRAST ENHANCEMENT OF DIGITAL MAMMOGRAMS USING AN IMPROVED WATERSHED SEGMENTATION","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2082323194","doi":"https://doi.org/10.1142/s0219467813500071","mag":"2082323194"},"language":"en","primary_location":{"id":"doi:10.1142/s0219467813500071","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219467813500071","pdf_url":null,"source":{"id":"https://openalex.org/S60080701","display_name":"International Journal of Image and Graphics","issn_l":"0219-4678","issn":["0219-4678","1793-6756"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Image and Graphics","raw_type":"journal-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/A5020866279","display_name":"A. Kaja Mohideen","orcid":"https://orcid.org/0000-0003-1329-3925"},"institutions":[{"id":"https://openalex.org/I4210109528","display_name":"PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH","ror":"https://ror.org/01sa9ng67","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210109528"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"ABUBACKER KAJA MOHIDEEN","raw_affiliation_strings":["Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India","institution_ids":["https://openalex.org/I4210109528"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109223018","display_name":"Kuttiannan Thangavel","orcid":null},"institutions":[{"id":"https://openalex.org/I141431873","display_name":"Periyar University","ror":"https://ror.org/05crs8s98","country_code":"IN","type":"education","lineage":["https://openalex.org/I141431873"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"KUTTIANNAN THANGAVEL","raw_affiliation_strings":["Department of Computer Science, Periyar University, Salem 636011, Tamil Nadu, India","Department of Computer Science Periyar University Salem - 636011, Tamil Nadu, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Periyar University, Salem 636011, Tamil Nadu, India","institution_ids":["https://openalex.org/I141431873"]},{"raw_affiliation_string":"Department of Computer Science Periyar University Salem - 636011, Tamil Nadu, India","institution_ids":["https://openalex.org/I141431873"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020866279"],"corresponding_institution_ids":["https://openalex.org/I4210109528"],"apc_list":null,"apc_paid":null,"fwci":0.8165,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.77237004,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"13","issue":"01","first_page":"1350007","last_page":"1350007"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9998000264167786,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9998000264167786,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7268805503845215},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7032645344734192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6713413596153259},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6124807596206665},{"id":"https://openalex.org/keywords/histogram-equalization","display_name":"Histogram equalization","score":0.5879306197166443},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5814077854156494},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5720264911651611},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5623851418495178},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5125527381896973},{"id":"https://openalex.org/keywords/adaptive-histogram-equalization","display_name":"Adaptive histogram equalization","score":0.489243745803833},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4417576789855957},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.41391828656196594},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21349552273750305}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7268805503845215},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7032645344734192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6713413596153259},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6124807596206665},{"id":"https://openalex.org/C136943445","wikidata":"https://www.wikidata.org/wiki/Q1970240","display_name":"Histogram equalization","level":4,"score":0.5879306197166443},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5814077854156494},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5720264911651611},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5623851418495178},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5125527381896973},{"id":"https://openalex.org/C30387639","wikidata":"https://www.wikidata.org/wiki/Q4680744","display_name":"Adaptive histogram equalization","level":5,"score":0.489243745803833},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4417576789855957},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.41391828656196594},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21349552273750305}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219467813500071","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219467813500071","pdf_url":null,"source":{"id":"https://openalex.org/S60080701","display_name":"International Journal of Image and Graphics","issn_l":"0219-4678","issn":["0219-4678","1793-6756"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Image and Graphics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.6499999761581421}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306481","display_name":"Prevent Cancer Foundation","ror":"https://ror.org/03jpt8f29"},{"id":"https://openalex.org/F4320320767","display_name":"University Grants Commission","ror":"https://ror.org/04p800546"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W116833144","https://openalex.org/W188623726","https://openalex.org/W1906444667","https://openalex.org/W1967642112","https://openalex.org/W1980573855","https://openalex.org/W1984606114","https://openalex.org/W1986276657","https://openalex.org/W1987645363","https://openalex.org/W2004217976","https://openalex.org/W2004264357","https://openalex.org/W2005143308","https://openalex.org/W2020402396","https://openalex.org/W2020887635","https://openalex.org/W2030120195","https://openalex.org/W2038843573","https://openalex.org/W2038952578","https://openalex.org/W2040434704","https://openalex.org/W2050883594","https://openalex.org/W2056565430","https://openalex.org/W2068128348","https://openalex.org/W2073171774","https://openalex.org/W2076293758","https://openalex.org/W2091452795","https://openalex.org/W2097528311","https://openalex.org/W2102427741","https://openalex.org/W2103763210","https://openalex.org/W2104254046","https://openalex.org/W2109388152","https://openalex.org/W2116930408","https://openalex.org/W2118002060","https://openalex.org/W2119604852","https://openalex.org/W2127570874","https://openalex.org/W2128518294","https://openalex.org/W2129829008","https://openalex.org/W2130771648","https://openalex.org/W2131006320","https://openalex.org/W2136584955","https://openalex.org/W2137618748","https://openalex.org/W2138358654","https://openalex.org/W2145388359","https://openalex.org/W2154392078","https://openalex.org/W2157840858","https://openalex.org/W2160754664","https://openalex.org/W2166739600","https://openalex.org/W2167338900","https://openalex.org/W2254058419","https://openalex.org/W2270451413","https://openalex.org/W2475862459","https://openalex.org/W4214540058","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2057981026","https://openalex.org/W2256021896","https://openalex.org/W2122866860","https://openalex.org/W2134986978","https://openalex.org/W2165297163","https://openalex.org/W2398368608","https://openalex.org/W1583737874","https://openalex.org/W1994424557","https://openalex.org/W2024449420","https://openalex.org/W3084145657"],"abstract_inverted_index":{"A":[0],"simple":[1,84],"edge-based":[2],"preprocessing":[3],"scheme":[4],"is":[5,34,50,71,81,89,106,127,168],"proposed":[6,24,102,123,150,177],"in":[7,91],"this":[8,101],"paper":[9],"for":[10],"contrast":[11,96,124],"enhancement":[12,125],"of":[13,100,116,148,186],"digital":[14],"mammogram":[15,38],"images":[16,39],"while":[17],"preserving":[18],"the":[19,31,37,42,46,55,75,122,131,137,145,153,161,165,180,184],"edges":[20],"more":[21],"accurately.":[22],"This":[23],"method":[25,105,178],"has":[26],"three":[27],"steps:":[28],"(i)":[29],"initially":[30],"breast":[32,56,76],"region":[33,49,57,80],"segmented":[35],"from":[36,54],"by":[40],"removing":[41],"film":[43],"artifacts,":[44],"(ii)":[45],"pectoral":[47,103],"muscle":[48],"identified":[51],"and":[52,64,78,114,121,142,164],"excluded":[53],"using":[58],"a":[59,172],"novel":[60],"adaptive":[61,95],"thresholding":[62],"method,":[63],"(iii)":[65],"an":[66],"Improved":[67],"Watershed":[68],"Segmentation":[69],"(IWS)":[70],"applied":[72],"to":[73,93],"segment":[74],"profile,":[77],"each":[79],"enhanced":[82],"with":[83,108,130,136,156,171,183],"histogram":[85],"equalization.":[86],"The":[87,98,140],"segmentation":[88],"performed":[90],"order":[92],"achieve":[94],"enhancement.":[97],"performance":[99,147,162,167,182],"removal":[104],"analyzed":[107,129],"two":[109],"measures:":[110],"Hausdorff":[111],"Distance":[112,119],"(HD)":[113],"Mean":[115],"Absolute":[117],"Error":[118],"(MAED),":[120],"approach":[126],"been":[128,169],"five":[132],"diverse":[133],"parameters":[134],"along":[135],"classification":[138,166],"accuracy.":[139,188],"experiments":[141],"results":[143,158],"show":[144],"potential":[146],"our":[149,176],"algorithm":[151],"over":[152],"existing":[154],"approaches":[155],"optimum":[157],"on":[159],"all":[160],"measure":[163],"evaluated":[170],"hybrid":[173],"neural":[174],"network,":[175],"proves":[179],"better":[181],"achievement":[185],"92%":[187]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}