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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZG2LgkFdY/MgCpj
Repositorysid.inpe.br/sibgrapi@80/2006/08.26.13.49
Last Update2006:08.26.13.49.20 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2006/08.26.13.49.21
Metadata Last Update2022:06.14.00.13.26 (UTC) administrator
DOI10.1109/SIBGRAPI.2006.31
Citation KeyRodrigues:2006:NoEnCA
TitleNon-Extensive Entropy for CAD Systems of Breast Cancer Images
FormatOn-line
Year2006
Access Date2024, May 03
Number of Files1
Size223 KiB
2. Context
AuthorRodrigues, Paulo Sérgio Silva
AffiliationNational Laboratory for Scientific Computing
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
e-Mail Addresspssr@lncc.br
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus, AM, Brazil
Date8-11 Oct. 2006
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:11:05 :: paulo -> administrator ::
2009-08-13 20:38:17 :: administrator -> banon ::
2010-08-28 20:02:26 :: banon -> administrator ::
2022-06-14 00:13:26 :: administrator -> :: 2006
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsCAD Tsallis Entropy Medical Image Analysis Breast Tumor
AbstractRecent statistics show that breast cancer is a major cause of death among women in all of the world. Hence, early diagnostic with Computer Aided Diagnosis (CAD) systems is a very important tool. This task is not easy due to poor ultrasound resolution and large amount of patient data size. Then, initial image segmentation is one of the most important and challenging task. Among several methods for medical image segmentation, the use of entropy for maximization the information between the foreground and background is a well known and applied technique. But, the traditional Shannon entropy fails to describe some physical systems with characteristics such as long-range and longtime interactions. Then, a new kind of entropy, called nonextensive entropy, has been proposed in the literature for generalizing the Shannon entropy. In this paper, we propose the use of non-extensive entropy, also called q-entropy, applied in a CAD system for breast cancer classification in ultrasound of mammographic exams. Our proposal combines the non-extensive entropy, a level set formulation and a Support Vector Machine framework to achieve better performance than the current literature offers. In order to validate our proposal, we have tested our automatic protocol in a data base of 250 breast ultrasound images (100 benign and 150 malignant). With a cross-validation protocol, we demonstrate systems accuracy, sensitivity, specificity, positive predictive value and negative predictive value as: 95%, 97%, 94%, 92% and 98%, respectively, in terms of ROC (Receiver Operating Characteristic) curves and Az areas.
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data URLhttp://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/MgCpj
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/MgCpj
Languageen
Target Filerodriguesr-CADSystems.pdf
User Grouppaulo
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Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46RFT7E
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.08.00.20 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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