Close

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PF8RQE
Repositorysid.inpe.br/sibgrapi/2017/08.18.03.29
Last Update2017:08.18.03.29.56 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.18.03.29.56
Metadata Last Update2022:06.14.00.08.45 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.47
Citation KeyJúniorAfonPalmPapa:2017:BaEsId
TitleBarrett’s Esophagus Identification Using Optimum-Path Forest
FormatOn-line
Year2017
Access Date2024, Apr. 29
Number of Files1
Size404 KiB
2. Context
Author1 Júnior, Luis Antonio de Souza
2 Afonso, Luis Cláudio Sugi
3 Palm, Christoph
4 Papa, João Paulo
Affiliation1 Federal University of São Carlos - UFScar
2 Federal University of São Carlos - UFScar
3 Ostbayerische Technische Hochschule Regensburg
4 São Paulo State University - UNESP
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressluis.souza@dc.ufscar.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-18 03:29:56 :: luis.souza@dc.ufscar.br -> administrator ::
2022-06-14 00:08:45 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsbarrett's esophagus
machine learning
pattern recognition
AbstractComputer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barretts esophagus (BE) is a common type of reflux that is not straightforward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barretts esophagus, with promising results and outperforming the wellknown Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-words that is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735 (SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Barrett’s Esophagus Identification...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Barrett’s Esophagus Identification...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 18/08/2017 00:29 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PF8RQE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PF8RQE
Languageen
Target FilePID4956031.pdf
User Groupluis.souza@dc.ufscar.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 6
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark 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


Close