@InProceedings{RittnerAppeLotu:2009:SeBrSt,
               author = "Rittner, Leticia and Appenzeller, Simone and Lotufo, Roberto de 
                         Alencar",
          affiliation = "{Faculdade de Engenharia El{\'e}trica - UNICAMP} and {Faculdade 
                         de Medicina - UNICAMP} and {Faculdade de Engenharia El{\'e}trica 
                         - UNICAMP}",
                title = "Segmentation of brain structures by watershed transform on 
                         tensorial morphological gradient of diffusion tensor imaging",
            booktitle = "Proceedings...",
                 year = "2009",
               editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Diffusion tensor imaging, MRI, Segmentation, Watershed transform, 
                         Mathematical morphology.",
             abstract = "Watershed transform on tensorial morphological gradient (TMG) is a 
                         new approach to segment diffusion tensor images (DTI). Since the 
                         TMG is able to express the tensorial dissimilarities in a single 
                         scalar image, the segmentation problem of DTI is then reduced to a 
                         scalar image segmentation problem. Therefore, it can be addressed 
                         by well-known segmentation techniques, such as the watershed 
                         transform. In other words, by computing the TMG of a DTI, and then 
                         using the hierarchical watershed transform, it is possible to 
                         segment brain structures, such as the corpus callosum, the 
                         ventricles and the cortico-spinal tracts, and use the results for 
                         subsequent quantitative analysis of DTI parameters. Experiments 
                         showed that segmentations obtained with the proposed approach are 
                         similar to the ones obtained by other segmentation techniques 
                         based on DTI and also segmentation methods based on other Magnetic 
                         Resonance Imaging (MRI) modalities. Since the proposed method, as 
                         opposed to the majority of the DTI based segmentation methods, 
                         does not require manual seed and/or surface placement, its results 
                         are highly repeatable. And unlike other methods that have 
                         sometimes four parameters to be adjusted, the only adjustable 
                         parameter is the number of regions in which the image should be 
                         segmented, making it simple and robust.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "11-14 Oct. 2009",
                  doi = "10.1109/SIBGRAPI.2009.36",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.36",
             language = "en",
                  ibi = "8JMKD3MGPBW4/35SNLLL",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW4/35SNLLL",
           targetfile = "PID950047.pdf",
        urlaccessdate = "2024, Apr. 28"
}