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@InProceedings{LeandroCÚsaCost:2006:DeBr3D,
               author = "Leandro, Jorge de Jesus Gomes and C{\'e}sar J{\'u}nior, Roberto 
                         Marcondes and Costa, Luciano da Fontoura",
          affiliation = "{Institute of Mathematics and Statistics - University of S{\~a}o 
                         Paulo - IME/USP} and {Institute of Mathematics and Statistics - 
                         University of S{\~a}o Paulo - IME/USP} and {Department of Physics 
                         and Informatics - Institute of Physics of S{\~a}o Carlos - USP}",
                title = "Determining the branchings of 3D structures from respective 2D 
                         projections",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "neurons, shape, branchings, crossings.",
             abstract = "This work describes a new framework for automatic extraction of 2D 
                         branching structures images obtained from 3D shapes, such as 
                         neurons and retinopathy images. The majority of methods for 
                         neuronal cell shape analysis that are based on the 2D contours of 
                         cells fall short of properly characterizing such cells because 
                         crossings among neuronal processes constrain the access of contour 
                         following algorithms to the innermost regions of the cell. The 
                         framework presented in this article addresses, possibly for the 
                         first time, the problem of determining the continuity along 
                         crossings, therefore granting to the contour following algorithm 
                         full access to all processes of the neuronal cell under analysis. 
                         First, the raw image is preprocessed so as to obtain an 
                         8-connected, one-pixel wide skeleton as well as a set of seed 
                         pixels for each subtree and all the branching/crossing regions. 
                         Then, for each seed pixel, the algorithm labels all valid 
                         neighbors, until a branching/crossing region is reached, when a 
                         decision about the proper continuation is taken based on the 
                         tangent continuity. The algorithm has shown robustness for images 
                         with parallel segments and low densities of branching/crossing 
                         densities. The problem of too high densities of branching/crossing 
                         regions can be addressed by using a suitable data structure. 
                         Successful experimental results using real data (neural cell 
                         images) are presented.",
  conference-location = "Manaus",
      conference-year = "8-11 Oct. 2006",
             language = "en",
           targetfile = "leandro-branching.pdf",
        urlaccessdate = "2020, Nov. 25"
}


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