%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2011/07.08.18.21
%2 sid.inpe.br/sibgrapi/2011/07.08.18.21.38
%@doi 10.1109/SIBGRAPI.2011.46
%T Watershed-based segmentation of the midsagittal section of the corpus callosum in diffusion MRI
%D 2011
%A Freitas, Pedro Ferro,
%A Rittner, Leticia,
%A Appenzeller, Simone,
%A Lotufo, Roberto de Alencar,
%@affiliation School of Electrical and Computer Engineering, University of Campinas - UNICAMP
%@affiliation School of Electrical and Computer Engineering, University of Campinas - UNICAMP
%@affiliation Department of Medicine, Rheumatology Unit, University of Campinas - UNICAMP
%@affiliation School of Electrical and Computer Engineering, University of Campinas - UNICAMP
%E Lewiner, Thomas,
%E Torres, Ricardo,
%B Conference on Graphics, Patterns and Images, 24 (SIBGRAPI)
%C Maceió, AL, Brazil
%8 28-31 Aug. 2011
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K corpus callosum, fractional anisotropy, diffusion tensor imaging, magnetic resonance image, segmentation, watershed transform.
%X 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
%3 example.pdf