Close
Metadata

Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/3868QFL
Repositorysid.inpe.br/sibgrapi/2010/08.28.15.30
Metadatasid.inpe.br/sibgrapi/2010/08.28.15.30.01
Sitesibgrapi.sid.inpe.br
Citation KeyZampirolliStraLorePaul:2010:ApGrAn
Author1 Zampirolli, Francisco de Assis
2 Stransky, Beatriz
3 Lorena, Ana Carolina
4 Paulon, Fábio Luis de Melo
Affiliation1 Universidade Federal do ABC
2 Universidade Federal do ABC
3 Universidade Federal do ABC
4 Universidade Federal do ABC
TitleSegmentation and classification of histological images - application of graph analysis and machine learning methods
Conference NameConference on Graphics, Patterns and Images, 23 (SIBGRAPI)
Year2010
EditorBellon, Olga
Esperança, Claudio
Book TitleProceedings
DateAug. 30 - Sep. 3, 2010
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationGramado
Keywordsimage analysis, mathematical morphology, graph analysis, machine learning, tissue.
AbstractThe characterization and quantitative description of histological images is not a simple problem. To reach a final diagnosis, usually the specialist relies on the analysis of characteristics easily observed, such as cells size, shape, staining and texture, but also depends on the hidden information of tissue localization, physiological and pathological mechanisms, clinical aspects, or other etiological agents. In this paper, Mathematical Morphology (MM) and Machine Learning (ML) methods were applied to characterize and classify histological images. MM techniques were employed for image analysis. The measurements obtained from image and graph analysis were fed into Machine Learning algorithms, which were designed and developed to automatically learn to recognize complex patterns and make intelligent decisions based on data. Specifically, a linear Support Vector Machine (SVM) was used to evaluate the discriminatory power of the used measures. The results show that the methodology was successful in characterizing and classifying the differences between the architectural organization of epithelial and adipose tissues. We believe that this approach can be also applied to classify and help.
Languageen
Tertiary TypeFull Paper
FormatPrinted, On-line.
Size1134 KiB
Number of Files1
Target Filearticle_sibgrapi_v8.pdf
Last Update2010:08.28.15.30.00 sid.inpe.br/banon/2001/03.30.15.38 fzampirolli@gmail.com
Metadata Last Update2010:10.01.04.19.37 sid.inpe.br/banon/2001/03.30.15.38 fzampirolli@gmail.com {D 2010}
Document Stagecompleted
Is the master or a copy?is the master
Mirrorsid.inpe.br/banon/2001/03.30.15.38.24
e-Mail Addressfzampirolli@gmail.com
User Groupfzampirolli@gmail.com
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
source Directory Contentthere are no files
agreement Directory Contentthere are no files
History2010-10-01 04:19:37 :: fzampirolli@gmail.com -> :: 2010
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 Date2020, Nov. 28

Close