Microcalcification detection using a fuzzy inference system and support vector machines
Breast cancer remains one of the deadliest diseases among women. Microcalcifications can be an early indicator of a breast cancer. Thus when they are present their detection during a screening test is very crucial. But due to their small size and low contrast in mammographies their detection is diff...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 315 |
|---|---|
| container_issue | |
| container_start_page | 312 |
| container_title | |
| container_volume | |
| creator | Kabbadj, Y. Regragui, F. Himmi, M. M. |
| description | Breast cancer remains one of the deadliest diseases among women. Microcalcifications can be an early indicator of a breast cancer. Thus when they are present their detection during a screening test is very crucial. But due to their small size and low contrast in mammographies their detection is difficult. Therefore many computer aided diagnosis mathods have been developped to help and assist rediologist during their screening tests. This paper presents a novel approach to detect microcalcifications on digitized mammaographies using fuzzy logic and support vector machines. Our method was tested on 16 mammograms from Mias database including both positive and negative cases. We have obtained very satisfactory results with a sensitivity of 99,60% and a specificity of 99,11% during the learning phase. |
| doi_str_mv | 10.1109/ICMCS.2012.6320216 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6320216</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6320216</ieee_id><sourcerecordid>6320216</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-dc8351b8bd2c6efb51c08202d813dbea383a91790629ffc78bfd666d405160933</originalsourceid><addsrcrecordid>eNo9UM1KxDAYjIigrvsCeskLbM2XtGlylOLPwi4eXPC4pMkXjWzT0rRC9-ktujiXmTnMMAwht8AyAKbv19W2ess4A55JwRkHeUauIZelgIIzef5vQOWXZJnSF5uhOAjNr8j7Nti-teZggw_WDKGN1OGA9leNKcQPaqgfj8eJhuixx2iRpikN2FATHU1j17X9QL_nSNvTxtjPEDHdkAtvDgmXJ16Q3dPjrnpZbV6f19XDZhU0G1bOKlFArWrHrURfF2DnZYw7BcLVaIQSRkOpmeTae1uq2jsppctZAZJpIRbk7q82IOK-60Nj-ml_-kH8AP3dUws</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Microcalcification detection using a fuzzy inference system and support vector machines</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Kabbadj, Y. ; Regragui, F. ; Himmi, M. M.</creator><creatorcontrib>Kabbadj, Y. ; Regragui, F. ; Himmi, M. M.</creatorcontrib><description>Breast cancer remains one of the deadliest diseases among women. Microcalcifications can be an early indicator of a breast cancer. Thus when they are present their detection during a screening test is very crucial. But due to their small size and low contrast in mammographies their detection is difficult. Therefore many computer aided diagnosis mathods have been developped to help and assist rediologist during their screening tests. This paper presents a novel approach to detect microcalcifications on digitized mammaographies using fuzzy logic and support vector machines. Our method was tested on 16 mammograms from Mias database including both positive and negative cases. We have obtained very satisfactory results with a sensitivity of 99,60% and a specificity of 99,11% during the learning phase.</description><identifier>ISBN: 1467315184</identifier><identifier>ISBN: 9781467315180</identifier><identifier>EISBN: 1467315206</identifier><identifier>EISBN: 1467315192</identifier><identifier>EISBN: 9781467315197</identifier><identifier>EISBN: 9781467315203</identifier><identifier>DOI: 10.1109/ICMCS.2012.6320216</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biomedical imaging ; Breast ; Breast cancer ; Computer Aided Detection Microcalcification Detection ; Diseases ; Educational institutions ; Fuzzy Inference Systems ; Image reconstruction ; Size measurement ; Support Vector Machines ; Surface treatment</subject><ispartof>2012 International Conference on Multimedia Computing and Systems, 2012, p.312-315</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6320216$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6320216$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kabbadj, Y.</creatorcontrib><creatorcontrib>Regragui, F.</creatorcontrib><creatorcontrib>Himmi, M. M.</creatorcontrib><title>Microcalcification detection using a fuzzy inference system and support vector machines</title><title>2012 International Conference on Multimedia Computing and Systems</title><addtitle>ICMCS</addtitle><description>Breast cancer remains one of the deadliest diseases among women. Microcalcifications can be an early indicator of a breast cancer. Thus when they are present their detection during a screening test is very crucial. But due to their small size and low contrast in mammographies their detection is difficult. Therefore many computer aided diagnosis mathods have been developped to help and assist rediologist during their screening tests. This paper presents a novel approach to detect microcalcifications on digitized mammaographies using fuzzy logic and support vector machines. Our method was tested on 16 mammograms from Mias database including both positive and negative cases. We have obtained very satisfactory results with a sensitivity of 99,60% and a specificity of 99,11% during the learning phase.</description><subject>Biomedical imaging</subject><subject>Breast</subject><subject>Breast cancer</subject><subject>Computer Aided Detection Microcalcification Detection</subject><subject>Diseases</subject><subject>Educational institutions</subject><subject>Fuzzy Inference Systems</subject><subject>Image reconstruction</subject><subject>Size measurement</subject><subject>Support Vector Machines</subject><subject>Surface treatment</subject><isbn>1467315184</isbn><isbn>9781467315180</isbn><isbn>1467315206</isbn><isbn>1467315192</isbn><isbn>9781467315197</isbn><isbn>9781467315203</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9UM1KxDAYjIigrvsCeskLbM2XtGlylOLPwi4eXPC4pMkXjWzT0rRC9-ktujiXmTnMMAwht8AyAKbv19W2ess4A55JwRkHeUauIZelgIIzef5vQOWXZJnSF5uhOAjNr8j7Nti-teZggw_WDKGN1OGA9leNKcQPaqgfj8eJhuixx2iRpikN2FATHU1j17X9QL_nSNvTxtjPEDHdkAtvDgmXJ16Q3dPjrnpZbV6f19XDZhU0G1bOKlFArWrHrURfF2DnZYw7BcLVaIQSRkOpmeTae1uq2jsppctZAZJpIRbk7q82IOK-60Nj-ml_-kH8AP3dUws</recordid><startdate>201205</startdate><enddate>201205</enddate><creator>Kabbadj, Y.</creator><creator>Regragui, F.</creator><creator>Himmi, M. M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201205</creationdate><title>Microcalcification detection using a fuzzy inference system and support vector machines</title><author>Kabbadj, Y. ; Regragui, F. ; Himmi, M. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-dc8351b8bd2c6efb51c08202d813dbea383a91790629ffc78bfd666d405160933</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Biomedical imaging</topic><topic>Breast</topic><topic>Breast cancer</topic><topic>Computer Aided Detection Microcalcification Detection</topic><topic>Diseases</topic><topic>Educational institutions</topic><topic>Fuzzy Inference Systems</topic><topic>Image reconstruction</topic><topic>Size measurement</topic><topic>Support Vector Machines</topic><topic>Surface treatment</topic><toplevel>online_resources</toplevel><creatorcontrib>Kabbadj, Y.</creatorcontrib><creatorcontrib>Regragui, F.</creatorcontrib><creatorcontrib>Himmi, M. M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kabbadj, Y.</au><au>Regragui, F.</au><au>Himmi, M. M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Microcalcification detection using a fuzzy inference system and support vector machines</atitle><btitle>2012 International Conference on Multimedia Computing and Systems</btitle><stitle>ICMCS</stitle><date>2012-05</date><risdate>2012</risdate><spage>312</spage><epage>315</epage><pages>312-315</pages><isbn>1467315184</isbn><isbn>9781467315180</isbn><eisbn>1467315206</eisbn><eisbn>1467315192</eisbn><eisbn>9781467315197</eisbn><eisbn>9781467315203</eisbn><abstract>Breast cancer remains one of the deadliest diseases among women. Microcalcifications can be an early indicator of a breast cancer. Thus when they are present their detection during a screening test is very crucial. But due to their small size and low contrast in mammographies their detection is difficult. Therefore many computer aided diagnosis mathods have been developped to help and assist rediologist during their screening tests. This paper presents a novel approach to detect microcalcifications on digitized mammaographies using fuzzy logic and support vector machines. Our method was tested on 16 mammograms from Mias database including both positive and negative cases. We have obtained very satisfactory results with a sensitivity of 99,60% and a specificity of 99,11% during the learning phase.</abstract><pub>IEEE</pub><doi>10.1109/ICMCS.2012.6320216</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 1467315184 |
| ispartof | 2012 International Conference on Multimedia Computing and Systems, 2012, p.312-315 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6320216 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biomedical imaging Breast Breast cancer Computer Aided Detection Microcalcification Detection Diseases Educational institutions Fuzzy Inference Systems Image reconstruction Size measurement Support Vector Machines Surface treatment |
| title | Microcalcification detection using a fuzzy inference system and support vector machines |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-20T07%3A35%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Microcalcification%20detection%20using%20a%20fuzzy%20inference%20system%20and%20support%20vector%20machines&rft.btitle=2012%20International%20Conference%20on%20Multimedia%20Computing%20and%20Systems&rft.au=Kabbadj,%20Y.&rft.date=2012-05&rft.spage=312&rft.epage=315&rft.pages=312-315&rft.isbn=1467315184&rft.isbn_list=9781467315180&rft_id=info:doi/10.1109/ICMCS.2012.6320216&rft_dat=%3Cieee_6IE%3E6320216%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1467315206&rft.eisbn_list=1467315192&rft.eisbn_list=9781467315197&rft.eisbn_list=9781467315203&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6320216&rfr_iscdi=true |