@InProceedings{OlivaIsoaMato:2008:BaEsHy,
               author = "Oliva, Dami{\'a}n Ernesto and Isoardi, Roberto Andr{\'e}s and 
                         Mato, Germ{\'a}n",
          affiliation = "Universidad Nacional de Buenos Aires, Argentina and Escuela de 
                         Medicina Nuclear, Mendoza, Argentina and Grupo F{\'{\i}}sica 
                         Estad{\'{\i}}stica, Centro At{\'o}mico Bariloche, Argentina",
                title = "Bayesian estimation of Hyperparameters in MRI through the Maximum 
                         Evidence Method",
            booktitle = "Proceedings...",
                 year = "2008",
               editor = "Jung, Cl{\'a}udio Rosito and Walter, Marcelo",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 21. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image segmentation, Bayesian analysis, MRI.",
             abstract = "Bayesian inference methods are commonly applied to the 
                         classification of brain Magnetic Resonance images (MRI). We use 
                         the Maximum Evidence (ME) approach to estimate the most probable 
                         parameters and hyperparameters for models that take into account 
                         discrete classes (DM) and models accounting for the partial volume 
                         effect (PVM). An approximate algorithm was developed for model 
                         optimization, since the exact image inference calculation is 
                         computationally expensive. The method was validated using 
                         simulated images and a digital phantom. We show that the Evidence 
                         is a very useful figure for error prediction, which is to be 
                         maximized respect to the hyperparameters. Additionally, it 
                         provides a tool to determine the most probable model given 
                         measured data.",
  conference-location = "Campo Grande, MS, Brazil",
      conference-year = "12-15 Oct. 2008",
                  doi = "10.1109/SIBGRAPI.2008.5",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2008.5",
             language = "en",
                  ibi = "6qtX3pFwXQZG2LgkFdY/UQ4Vi",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/UQ4Vi",
           targetfile = "Oliva-Bayesian.pdf",
        urlaccessdate = "2024, May 02"
}