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Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/3A3SG5H
Repositorysid.inpe.br/sibgrapi/2011/07.12.08.58
Metadatasid.inpe.br/sibgrapi/2011/07.12.08.58.48
Sitesibgrapi.sid.inpe.br
Citation KeyDesernoSoirOlivArau:2011:ToCoDi
Author1 Deserno, Thomas M.
2 Soiron, Michael
3 Oliveira, Julia E. E. de
4 Araujo, Arnaldo de A.
Affiliation1 Department of Medical Informatics, RWTH Aachen University, Aachen, Germany
2 Department of Medical Informatics, RWTH Aachen University, Aachen, Germany
3 Department of Computer Science, Universidade Federal de Minas Gerais Belo Horizonte, MG, Brazil
4 Department of Computer Science, Universidade Federal de Minas Gerais Belo Horizonte, MG, Brazil
TitleTowards computer-aided diagnostics of screening mammography using content-based image retrieval
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Year2011
EditorLewiner, Thomas
Torres, Ricardo
Book TitleProceedings
DateAug. 28 - 31, 2011
Publisher CityLos Alamitos
PublisherIEEE Computer Society Conference Publishing Services
Conference LocationMaceió
KeywordsContent-based image retrieval, Computer-aided diagnosis, Principal component analysis, Support vector machine, Mammography, Breast lesion, Breast density.
AbstractScreening mammography has been established worldwide for early detection of breast cancer, one of the main causes of death among women in occidental countries. In this paper, we aim at moving towards computer-aided diagnostics of screening mammography. Tissue and lesion are classified using the methodology of content-based image retrieval. In addition, we aim at comprehensive evaluation and have established a large database of annotated reference images (ground truth), which has been merged and unified from different sources publicly available to research. In total, 10,509 mammographic images have been collected from the different sources. From this, 3,375 images are provided with one and 430 radiographs with more than one chain code annotations. This data supports experiments with up to 12 classes, and 233 images per class if a equal distribution is required. Using a two-dimensional principal component analysis with four eigenvalues and a support vector machine with Gaussian kernel for feature extraction and image retrieval, respectively, the precision of computer-aided diagnosis is above 80%. It therefore may be used as second opinion in screening mammography.
Languageen
Tertiary TypeFull Paper
FormatDVD, On-line.
Size1639 KiB
Number of Files1
Target FileDeserno-2011.pdf
Last Update2011:07.12.08.58.48 sid.inpe.br/banon/2001/03.30.15.38 deserno@ieee.org
Metadata Last Update2011:07.23.15.36.13 sid.inpe.br/banon/2001/03.30.15.38 deserno@ieee.org {D 2011}
Document Stagecompleted
Is the master or a copy?is the master
Mirrorsid.inpe.br/banon/2001/03.30.15.38.24
e-Mail Addressdeserno@ieee.org
User Groupdeserno@ieee.org
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
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History2011-07-23 15:36:13 :: deserno@ieee.org -> :: 2011
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2019, Dec. 07

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