Reference Type | Conference Proceedings |
Identifier | 8JMKD3MGPBW34M/3A3ALCL |
Repository | sid.inpe.br/sibgrapi/2011/07.08.18.21 |
Metadata | sid.inpe.br/sibgrapi/2011/07.08.18.21.38 |
Site | sibgrapi.sid.inpe.br |
Citation Key | FreitasRittAppeLotu:2011:WaSeMi |
Author | 1 Freitas, Pedro Ferro 2 Rittner, Leticia 3 Appenzeller, Simone 4 Lotufo, Roberto de Alencar |
Affiliation | 1 School of Electrical and Computer Engineering, University of Campinas - UNICAMP 2 School of Electrical and Computer Engineering, University of Campinas - UNICAMP 3 Department of Medicine, Rheumatology Unit, University of Campinas - UNICAMP 4 School of Electrical and Computer Engineering, University of Campinas - UNICAMP |
Title | Watershed-based segmentation of the midsagittal section of the corpus callosum in diffusion MRI  |
Conference Name | Conference on Graphics, Patterns and Images, 24 (SIBGRAPI) |
Year | 2011 |
Editor | Lewiner, Thomas Torres, Ricardo |
Book Title | Proceedings |
Date | Aug. 28 - 31, 2011 |
Publisher City | Los Alamitos |
Publisher | IEEE Computer Society Conference Publishing Services |
Conference Location | Maceió |
Keywords | corpus callosum, fractional anisotropy, diffusion tensor imaging, magnetic resonance image, segmentation, watershed transform. |
Abstract | The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres. The corpus callosum is related to several neurodegenerative diseases and, as segmentation is usually the first step for studies in this structure, it is important to have a robust method for CC segmentation. We propose here a new approach for fully automatic segmentation of the CC in the magnetic resonance diffusion tensor images. The method uses the watershed transform and is performed on the fractional anisotropy (FA) map weighted by the projection of the principal eigenvector in the left-right direction. It first computes the section of the CC in the midsagittal slice and uses it as a seed for the 3D volume segmentation. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC without any user intervention, with great results when compared to manual segmentation. Since it is simple, fast and does not require parameter settings, the proposed method is well suited for clinical applications. |
Language | en |
Tertiary Type | Full Paper |
Format | DVD, On-line. |
Size | 518 KiB |
Number of Files | 1 |
Target File | example.pdf |
Last Update | 2011:07.08.18.21.38 sid.inpe.br/banon/2001/03.30.15.38 pedroferro86@gmail.com |
Metadata Last Update | 2011:07.23.15.36.12 sid.inpe.br/banon/2001/03.30.15.38 pedroferro86@gmail.com {D 2011} |
Document Stage | completed |
Is the master or a copy? | is the master |
Mirror | sid.inpe.br/banon/2001/03.30.15.38.24 |
e-Mail Address | pedroferro86@gmail.com |
User Group | pedroferro86@gmail.com |
Visibility | shown |
Transferable | 1 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Content Type | External Contribution |
source Directory Content | there are no files |
agreement Directory Content | |
History | 2011-07-23 15:36:12 :: pedroferro86@gmail.com -> :: 2011 |
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Access Date | 2019, Dec. 09 |