Breast tissue removal for enhancing microcalcification cluster detection in mammograms

Breast tissue removal for enhancing microcalcification cluster detection in mammograms

In this paper, we propose a novel normal breast tissue removal approach using sparse representation (SR), in order to emphasize subtle microcalcifications (MCs) for MC cluster (MCC) detection in mammograms. The proposed method adopts SR to estimate normal breast tissue texture only; such that the di...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Baddar, Wissam J., Dae Hoe Kim, Yong Man Ro
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Breast tissue
Cancer
Delta-sigma modulation
Dictionaries
Image reconstruction
Sensitivity
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 366
container_issue
container_start_page 363
container_title
container_volume
creator Baddar, Wissam J.
Dae Hoe Kim
Yong Man Ro
description In this paper, we propose a novel normal breast tissue removal approach using sparse representation (SR), in order to emphasize subtle microcalcifications (MCs) for MC cluster (MCC) detection in mammograms. The proposed method adopts SR to estimate normal breast tissue texture only; such that the difference between estimated image and the original image can emphasize subtle MCs. Comparative experiments have been conducted to validate the effectiveness of the proposed preprocessing with the publicly available DDSM database. The experimental results showed that the MCC detection performances in terms of FROC were improved with the proposed approach compared with the commonly used wavelet decomposition approach. Furthermore, the improved detection performance increases the overall performance for the malignant MCC classification.
doi_str_mv 10.1109/BHI.2014.6864378
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_RIE</sourceid><recordid>TN_cdi_ieee_primary_6864378</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6864378</ieee_id><sourcerecordid>6864378</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-c8446233d0372892b6fa56708ded4690741da031d058d28eee8a7f09fb0419d33</originalsourceid><addsrcrecordid>eNo1kEtLAzEcxKMoWGvvgpd8gV3_eTSPoy3WFgpeiteS5lEjm11JUsFv76J1LjP8BuYwCN0TaAkB_bhYb1oKhLdCCc6kukC3hEutKWFEX6IJJUI1lIK6-s9E8xs0K-UDRqkRaTFBb4vsTam4xlJOHmefhi_T4TBk7Pt309vYH3GKNg_WdDaGaE2NQ49tdyrVZ-x89faXxB4nk9JwzCaVO3QdTFf87OxTtFs975brZvv6slk-bZuooTZWcS4oYw6YpErTgwhmLiQo5x0XGiQnzgAjDubKUeW9V0YG0OEAnGjH2BQ9_M3Gsdt_5phM_t6fH2E_tpJTZA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Breast tissue removal for enhancing microcalcification cluster detection in mammograms</title><source>IEEE Electronic Library (IEL)</source><creator>Baddar, Wissam J. ; Dae Hoe Kim ; Yong Man Ro</creator><creatorcontrib>Baddar, Wissam J. ; Dae Hoe Kim ; Yong Man Ro</creatorcontrib><description>In this paper, we propose a novel normal breast tissue removal approach using sparse representation (SR), in order to emphasize subtle microcalcifications (MCs) for MC cluster (MCC) detection in mammograms. The proposed method adopts SR to estimate normal breast tissue texture only; such that the difference between estimated image and the original image can emphasize subtle MCs. Comparative experiments have been conducted to validate the effectiveness of the proposed preprocessing with the publicly available DDSM database. The experimental results showed that the MCC detection performances in terms of FROC were improved with the proposed approach compared with the commonly used wavelet decomposition approach. Furthermore, the improved detection performance increases the overall performance for the malignant MCC classification.</description><identifier>ISSN: 2168-2194</identifier><identifier>EISSN: 2168-2208</identifier><identifier>EISBN: 1479921319</identifier><identifier>EISBN: 9781479921317</identifier><identifier>DOI: 10.1109/BHI.2014.6864378</identifier><language>eng</language><publisher>IEEE</publisher><subject>Breast tissue ; Cancer ; Delta-sigma modulation ; Dictionaries ; Image reconstruction ; Sensitivity</subject><ispartof>IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014, p.363-366</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6864378$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,792,23909,23910,25118,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6864378$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Baddar, Wissam J.</creatorcontrib><creatorcontrib>Dae Hoe Kim</creatorcontrib><creatorcontrib>Yong Man Ro</creatorcontrib><title>Breast tissue removal for enhancing microcalcification cluster detection in mammograms</title><title>IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)</title><addtitle>BHI</addtitle><description>In this paper, we propose a novel normal breast tissue removal approach using sparse representation (SR), in order to emphasize subtle microcalcifications (MCs) for MC cluster (MCC) detection in mammograms. The proposed method adopts SR to estimate normal breast tissue texture only; such that the difference between estimated image and the original image can emphasize subtle MCs. Comparative experiments have been conducted to validate the effectiveness of the proposed preprocessing with the publicly available DDSM database. The experimental results showed that the MCC detection performances in terms of FROC were improved with the proposed approach compared with the commonly used wavelet decomposition approach. Furthermore, the improved detection performance increases the overall performance for the malignant MCC classification.</description><subject>Breast tissue</subject><subject>Cancer</subject><subject>Delta-sigma modulation</subject><subject>Dictionaries</subject><subject>Image reconstruction</subject><subject>Sensitivity</subject><issn>2168-2194</issn><issn>2168-2208</issn><isbn>1479921319</isbn><isbn>9781479921317</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kEtLAzEcxKMoWGvvgpd8gV3_eTSPoy3WFgpeiteS5lEjm11JUsFv76J1LjP8BuYwCN0TaAkB_bhYb1oKhLdCCc6kukC3hEutKWFEX6IJJUI1lIK6-s9E8xs0K-UDRqkRaTFBb4vsTam4xlJOHmefhi_T4TBk7Pt309vYH3GKNg_WdDaGaE2NQ49tdyrVZ-x89faXxB4nk9JwzCaVO3QdTFf87OxTtFs975brZvv6slk-bZuooTZWcS4oYw6YpErTgwhmLiQo5x0XGiQnzgAjDubKUeW9V0YG0OEAnGjH2BQ9_M3Gsdt_5phM_t6fH2E_tpJTZA</recordid><startdate>201406</startdate><enddate>201406</enddate><creator>Baddar, Wissam J.</creator><creator>Dae Hoe Kim</creator><creator>Yong Man Ro</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201406</creationdate><title>Breast tissue removal for enhancing microcalcification cluster detection in mammograms</title><author>Baddar, Wissam J. ; Dae Hoe Kim ; Yong Man Ro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-c8446233d0372892b6fa56708ded4690741da031d058d28eee8a7f09fb0419d33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Breast tissue</topic><topic>Cancer</topic><topic>Delta-sigma modulation</topic><topic>Dictionaries</topic><topic>Image reconstruction</topic><topic>Sensitivity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baddar, Wissam J.</creatorcontrib><creatorcontrib>Dae Hoe Kim</creatorcontrib><creatorcontrib>Yong Man Ro</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>Baddar, Wissam J.</au><au>Dae Hoe Kim</au><au>Yong Man Ro</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Breast tissue removal for enhancing microcalcification cluster detection in mammograms</atitle><btitle>IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)</btitle><stitle>BHI</stitle><date>2014-06</date><risdate>2014</risdate><spage>363</spage><epage>366</epage><pages>363-366</pages><issn>2168-2194</issn><eissn>2168-2208</eissn><eisbn>1479921319</eisbn><eisbn>9781479921317</eisbn><abstract>In this paper, we propose a novel normal breast tissue removal approach using sparse representation (SR), in order to emphasize subtle microcalcifications (MCs) for MC cluster (MCC) detection in mammograms. The proposed method adopts SR to estimate normal breast tissue texture only; such that the difference between estimated image and the original image can emphasize subtle MCs. Comparative experiments have been conducted to validate the effectiveness of the proposed preprocessing with the publicly available DDSM database. The experimental results showed that the MCC detection performances in terms of FROC were improved with the proposed approach compared with the commonly used wavelet decomposition approach. Furthermore, the improved detection performance increases the overall performance for the malignant MCC classification.</abstract><pub>IEEE</pub><doi>10.1109/BHI.2014.6864378</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2168-2194
ispartof IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014, p.363-366
issn 2168-2194
2168-2208
language eng
recordid cdi_ieee_primary_6864378
source IEEE Electronic Library (IEL)
subjects Breast tissue
Cancer
Delta-sigma modulation
Dictionaries
Image reconstruction
Sensitivity
title Breast tissue removal for enhancing microcalcification cluster detection in mammograms
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T22%3A55%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Breast%20tissue%20removal%20for%20enhancing%20microcalcification%20cluster%20detection%20in%20mammograms&rft.btitle=IEEE-EMBS%20International%20Conference%20on%20Biomedical%20and%20Health%20Informatics%20(BHI)&rft.au=Baddar,%20Wissam%20J.&rft.date=2014-06&rft.spage=363&rft.epage=366&rft.pages=363-366&rft.issn=2168-2194&rft.eissn=2168-2208&rft_id=info:doi/10.1109/BHI.2014.6864378&rft_dat=%3Cieee_RIE%3E6864378%3C/ieee_RIE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1479921319&rft.eisbn_list=9781479921317&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6864378&rfr_iscdi=true