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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45E4SE5
Repositorysid.inpe.br/sibgrapi/2021/09.13.18.23
Last Update2021:09.13.18.23.57 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.13.18.23.57
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeySantos:2021:SeSeSk
TitleSemi-automatic Segmentation of Skin Lesions based on Superpixels and Hybrid Texture Information
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size20009 KiB
2. Context
AuthorSantos, Elineide Silva dos
AffiliationFederal University of Piauí
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addresselineide.silva.inf@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2021-09-13 18:23:57 :: elineide.silva.inf@gmail.com -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsDermatoscopic image segmentation. Computer-aided diagnosis. Skin lesion. Texture information
AbstractThis article exposes a semi-automatic method with the potential to aid the doctor while supervising the progression of skin lesions. The proposed methodology pre-segments skin lesions using the SLIC0 algorithm for the generation of superpixels. Following this, each superpixel is represented using a descriptor constructed of a mix from GLCM and Tamura texture features. The feature's gain ratios were utilized to choose the data applied in the semi-supervised clustering algorithm Seeded Fuzzy C-means. This algorithm uses certain specialist-marked regions to group the superpixels into lesion or background regions. Finally, the segmented image undergoes a post-processing step to eliminate sharp edges. The experiments were performed on a total of 3974 images. We used the 2995 images from PH2, DermIS and ISIC 2018 datasets to establish our method's specifications and the 979 images from ISIC 2016 and ISIC 2017 datasets for performance analysis. Our experiments demonstrate that by manually identifying a few percentages of the generated superpixels, the proposed approach reaches an average accuracy of 95.97%, thus giving a superior performance to the techniques presented in the literature. Even though the proposed method requires physicians' intervention, they can obtain segmentation results similar to manual segmentation from a significantly less time-consuming task.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Semi-automatic Segmentation of...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45E4SE5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45E4SE5
Languageen
Target FileWTD___SIBGRAPI_2021___Elineide.pdf
User Groupelineide.silva.inf@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi 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 versiontype volume


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