@Article{CagnoniDobrPoliYanc:1999:GeAlIn,
               author = "Cagnoni, S. and Dobrzeniecki, A. B. and Poli, R. and Yanch, J. 
                         C.",
                title = "Genetic algorithm-based interactive segmentation of 3D medical 
                         images",
              journal = "Image and Vision Computing",
                 year = "1999",
               volume = "17",
               number = "12",
                pages = "881--895",
                month = "October",
                 note = "{}",
             keywords = "genetic algorithm, elastic contour, filter.",
             abstract = "This article describes a method for evolving adaptive procedures 
                         for the contour-based segmentation of anatomical structures in 3D 
                         medical data sets. With this method, the user first manually 
                         traces one or more 2D contours of an anatomical structure of 
                         interest on parallel planes arbitrarily cutting the data set. Such 
                         contours are then used as training examples for a genetic 
                         algorithm to evolve a contour detector. By applying the detector 
                         to the rest of the image sequence it is possible to obtain a full 
                         segmentation of the structure. The same detector can then be used 
                         to segment other image sequences of the same sort. Segmentation is 
                         driven by a contour-tracking strategy that relies on an 
                         elastic-contour model whose parameters are also optimized by the 
                         genetic algorithm. We report results obtained on a 
                         software-generated phantom and on real tomographic images of 
                         different sorts.",
           copyholder = "faria - tese",
        urlaccessdate = "12 maio 2024"
}