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@InProceedings{MachadoCampGee:2000:PrInMa,
               author = "Machado, Alexei Manso Corr{\^e}a and Campos, Mario Fernando 
                         Montenegro and Gee, James",
                title = "Probabilistic intensity mapping in MRI image registration",
                 year = "2000",
               editor = "Carvalho, Paulo Cezar Pinto and Walter, Marcelo",
                pages = "74--81",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 13. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
                 note = "The conference was held in Gramado, RS, Brazil, from October 17 to 
                         20.",
             keywords = "probability, probabilistic intensity mapping, MRI image 
                         registration, MR sensors, intensity distortions, image 
                         registration, likelihood modeling, similarity metrics, image 
                         matching, morphological constraints, noise, experiments.",
             abstract = "In this work, we present a method which is able to relate 
                         different MR sensors with respect to intensity distortions in the 
                         output images. For the important problem of image registration, 
                         the method makes possible a principled approach to likelihood 
                         modeling or the construction of similarity metrics. Likelihood 
                         models can be used as prior knowledge of the relationship between 
                         intensities in both images, providing a fundamental information 
                         resource for image registration. A poor model of the intensity 
                         mapping for the image pair to be matched may lead to false 
                         matches, regardless of the prior morphological constraints assumed 
                         and will bias all subsequent analyses. A formal analysis of 
                         robustness under different kinds of noise is also provided and the 
                         findings compared to other relevant similarity metrics. 
                         Experiments are controlled based on the application of synthetic 
                         spatial and intensity deformations that guarantee a fiducial basis 
                         for comparison.",
  conference-location = "Gramado, RS, Brazil",
      conference-year = "October",
         organisation = "SBC - Brazilian Computer Society",
           targetfile = "74-81.pdf",
        urlaccessdate = "2020, Nov. 28"
}


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