Quantitative assessment of breast dense tissue on mammograms
This paper presents a method to automatically segment dense tissue from mammography images. The method uses unsupervised learning and multiple levels of detail (LoD) to adapt itself to various image characteristics and generate robust segmentation result, which can potentially be of great help in su...
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| creator | Zhiqiang Lao Zhimin Huo |
| description | This paper presents a method to automatically segment dense tissue from mammography images. The method uses unsupervised learning and multiple levels of detail (LoD) to adapt itself to various image characteristics and generate robust segmentation result, which can potentially be of great help in surveillance breast health and detecting breast cancer at early stage. Multiple LoD used in the method include 1. Initial entropy maximum based thresholding (low LoD); 2. FCM based ¿soft-threshold¿ estimation (mid LoD); 3 Pixel-wise dense tissue feature evaluation (high LodD). The performance validation, based on 220 cases from 6 different mammographic data sets, shows a strong correlation between computer and clinical BI-RADS ratings. |
| doi_str_mv | 10.1109/ICIP.2009.5413991 |
| format | Conference Proceeding |
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The method uses unsupervised learning and multiple levels of detail (LoD) to adapt itself to various image characteristics and generate robust segmentation result, which can potentially be of great help in surveillance breast health and detecting breast cancer at early stage. Multiple LoD used in the method include 1. Initial entropy maximum based thresholding (low LoD); 2. FCM based ¿soft-threshold¿ estimation (mid LoD); 3 Pixel-wise dense tissue feature evaluation (high LodD). The performance validation, based on 220 cases from 6 different mammographic data sets, shows a strong correlation between computer and clinical BI-RADS ratings.</description><identifier>ISSN: 1522-4880</identifier><identifier>ISBN: 9781424456536</identifier><identifier>ISBN: 1424456533</identifier><identifier>EISSN: 2381-8549</identifier><identifier>EISBN: 9781424456550</identifier><identifier>EISBN: 9781424456543</identifier><identifier>EISBN: 142445655X</identifier><identifier>EISBN: 1424456541</identifier><identifier>DOI: 10.1109/ICIP.2009.5413991</identifier><language>eng</language><publisher>IEEE</publisher><subject>Breast cancer ; Breast density quantification ; Cancer detection ; Character generation ; computer aided diagnosis ; Entropy ; Fuzzy C-means ; Image generation ; Image segmentation ; Mammography ; parenchymal patterns ; Robustness ; Surveillance ; Unsupervised learning</subject><ispartof>2009 16th IEEE International Conference on Image Processing (ICIP), 2009, p.2605-2608</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/5413991$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5413991$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhiqiang Lao</creatorcontrib><creatorcontrib>Zhimin Huo</creatorcontrib><title>Quantitative assessment of breast dense tissue on mammograms</title><title>2009 16th IEEE International Conference on Image Processing (ICIP)</title><addtitle>ICIP</addtitle><description>This paper presents a method to automatically segment dense tissue from mammography images. The method uses unsupervised learning and multiple levels of detail (LoD) to adapt itself to various image characteristics and generate robust segmentation result, which can potentially be of great help in surveillance breast health and detecting breast cancer at early stage. Multiple LoD used in the method include 1. Initial entropy maximum based thresholding (low LoD); 2. FCM based ¿soft-threshold¿ estimation (mid LoD); 3 Pixel-wise dense tissue feature evaluation (high LodD). The performance validation, based on 220 cases from 6 different mammographic data sets, shows a strong correlation between computer and clinical BI-RADS ratings.</description><subject>Breast cancer</subject><subject>Breast density quantification</subject><subject>Cancer detection</subject><subject>Character generation</subject><subject>computer aided diagnosis</subject><subject>Entropy</subject><subject>Fuzzy C-means</subject><subject>Image generation</subject><subject>Image segmentation</subject><subject>Mammography</subject><subject>parenchymal patterns</subject><subject>Robustness</subject><subject>Surveillance</subject><subject>Unsupervised learning</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9781424456536</isbn><isbn>1424456533</isbn><isbn>9781424456550</isbn><isbn>9781424456543</isbn><isbn>142445655X</isbn><isbn>1424456541</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkMtKxDAYheMNrOM8gLjJC7T-uTUJuJFh1MKACroekvaPREwrTUbw7R1wNq4OnO_wLQ4hVwwaxsDedKvuueEAtlGSCWvZEVlabZjkUqpWKTgmFReG1UZJe_KPifaUVExxXktj4Jxc5PwBwIEJVpHbl50bSyyuxG-kLmfMOeFY6BSon9HlQgccM9ISc94hnUaaXErT--xSviRnwX1mXB5yQd7u16-rx3rz9NCt7jZ1ZFqVuufOac8xDKiEMpy1YdDgQfZaCN22odd6sLoHUGit8UoL6_eDYDzum14syPWfNyLi9muOyc0_28MR4hcewU2A</recordid><startdate>200911</startdate><enddate>200911</enddate><creator>Zhiqiang Lao</creator><creator>Zhimin Huo</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200911</creationdate><title>Quantitative assessment of breast dense tissue on mammograms</title><author>Zhiqiang Lao ; Zhimin Huo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c2aa7b2efde5358216fd70b04c733766fc77d97c005e998b5739b70bf8be5e9c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Breast cancer</topic><topic>Breast density quantification</topic><topic>Cancer detection</topic><topic>Character generation</topic><topic>computer aided diagnosis</topic><topic>Entropy</topic><topic>Fuzzy C-means</topic><topic>Image generation</topic><topic>Image segmentation</topic><topic>Mammography</topic><topic>parenchymal patterns</topic><topic>Robustness</topic><topic>Surveillance</topic><topic>Unsupervised learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhiqiang Lao</creatorcontrib><creatorcontrib>Zhimin Huo</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhiqiang Lao</au><au>Zhimin Huo</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Quantitative assessment of breast dense tissue on mammograms</atitle><btitle>2009 16th IEEE International Conference on Image Processing (ICIP)</btitle><stitle>ICIP</stitle><date>2009-11</date><risdate>2009</risdate><spage>2605</spage><epage>2608</epage><pages>2605-2608</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9781424456536</isbn><isbn>1424456533</isbn><eisbn>9781424456550</eisbn><eisbn>9781424456543</eisbn><eisbn>142445655X</eisbn><eisbn>1424456541</eisbn><abstract>This paper presents a method to automatically segment dense tissue from mammography images. The method uses unsupervised learning and multiple levels of detail (LoD) to adapt itself to various image characteristics and generate robust segmentation result, which can potentially be of great help in surveillance breast health and detecting breast cancer at early stage. Multiple LoD used in the method include 1. Initial entropy maximum based thresholding (low LoD); 2. FCM based ¿soft-threshold¿ estimation (mid LoD); 3 Pixel-wise dense tissue feature evaluation (high LodD). The performance validation, based on 220 cases from 6 different mammographic data sets, shows a strong correlation between computer and clinical BI-RADS ratings.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2009.5413991</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, p.2605-2608 |
| issn | 1522-4880 2381-8549 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Breast cancer Breast density quantification Cancer detection Character generation computer aided diagnosis Entropy Fuzzy C-means Image generation Image segmentation Mammography parenchymal patterns Robustness Surveillance Unsupervised learning |
| title | Quantitative assessment of breast dense tissue on mammograms |
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