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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZG2LgkFdY/V5kVG
Repositorysid.inpe.br/sibgrapi@80/2008/08.06.16.30
Last Update2008:08.06.16.30.10 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2008/08.06.16.30.11
Metadata Last Update2024:04.05.23.31.29 (UTC) administrator
DOI10.1109/SIBGRAPI.2008.17
Citation KeyRittnerLotu:2008:DiTeIm
TitleDiffusion tensor imaging segmentation by watershed transform on tensorial morphological gradient
FormatPrinted, On-line.
Year2008
Access Date2024, Apr. 28
Number of Files1
Size507 KiB
2. Context
Author1 Rittner, Leticia
2 Lotufo, Roberto A.
Affiliation1 FEEC - UNICAMP
2 FEEC - UNICAMP
EditorJung, Cláudio Rosito
Walter, Marcelo
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 21 (SIBGRAPI)
Conference LocationCampo Grande, MS, Brazil
Date12-15 Oct. 2008
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-08-06 16:30:11 :: lrittner@dca.fee.unicamp.br -> administrator ::
2009-08-13 20:39:02 :: administrator -> lrittner@dca.fee.unicamp.br ::
2010-08-28 20:03:23 :: lrittner@dca.fee.unicamp.br -> administrator ::
2024-04-05 23:31:29 :: administrator -> :: 2008
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDTI
Image segmentation
Mathematical morphology
Watershed transform
AbstractWhile scalar image segmentation has been studied extensively, diffusion tensor imaging (DTI) segmentation is a relatively new and challenging task. Either existent segmentation methods have to be adapted to deal with tensorial information or completely new segmentation methods have to be developed to accomplish this task. Alternatively, what this work proposes is the computation of a tensorial morphological gradient of DTI, and its segmentation by IFT-based watershed transform. The strength of the proposed segmentation method is its simplicity and robustness, consequences of the tensorial morphological gradient computation. It enables the use, not only of well known algorithms and tools from the mathematical morphology, but also of any other segmentation method to segment DTI, since the computation of the tensorial morphological gradient transforms tensorial images in scalar ones. In order to validate the proposed method, synthetic diffusion tensor fields were generated, and Gaussian noise was added to them. A set of real DTI was also used in the method validation. All segmentation results confirmed that the proposed method is capable to segment different diffusion tensor images, including noisy and real ones.
Arrangement 1MM > Diffusion tensor imaging...
Arrangement 2urlib.net > SDLA > Fonds > SIBGRAPI 2008 > Diffusion tensor imaging...
Arrangement 3urlib.net > SDLA > Fonds > Full Index > Diffusion tensor imaging...
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/V5kVG
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/V5kVG
Languageen
Target Filerittner_dtisegmentation.pdf
User Grouplrittner@dca.fee.unicamp.br
administrator
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPCW/4AUUH9L
8JMKD3MGPEW34M/46SG4TH
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.14.04.55 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage e-mailaddress 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


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