@InProceedings{ThomazAgOlDuBuGiRu:2006:MuLiAp,
               author = "Thomaz, Carlos and Aguiar, Nelson and Oliveira, Sergio and Duran, 
                         Fabio and Busatto, Geraldo and Gillies, Duncan and Rueckert, 
                         Daniel",
          affiliation = "Department of Electrical Engineering, Centro Universitario da FEI, 
                         S{\~a}o Paulo, Brazil and Department of Electrical Engineering, 
                         Centro Universitario da FEI, S{\~a}o Paulo, Brazil and Department 
                         of Electrical Engineering, Centro Universitario da FEI, S{\~a}o 
                         Paulo, Brazil and Departments of Psychiatry and Radiology, Faculty 
                         of Medicine, University of S{\~a}o Paulo, Brazil and Departments 
                         of Psychiatry and Radiology, Faculty of Medicine, University of 
                         S{\~a}o Paulo, Brazil and Department of Computing, Imperial 
                         College, London, UK and Department of Computing, Imperial College, 
                         London, UK",
                title = "Extracting Discriminative Information from Medical Images: A 
                         Multivariate Linear Approach",
            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 = "Statistical pattern recognition, Medical image computing.",
             abstract = "Statistical discrimination methods are suitable not only for 
                         classification but also for characterisation of differences 
                         between a reference group of patterns and the population under 
                         investigation. In the last years, statistical methods have been 
                         proposed to classify and analyse morphological and anatomical 
                         structures of medical images. Most of these techniques work in 
                         high-dimensional spaces of particular features such as shapes or 
                         statistical parametric maps and have overcome the difficulty of 
                         dealing with the inherent high dimensionality of medical images by 
                         analysing segmented structures individually or performing 
                         hypothesis tests on each feature separately. In this paper, we 
                         present a general multivariate linear framework to identify and 
                         analyse the most discriminating hyper-plane separating two 
                         populations. The goal is to analyse all the intensity features 
                         simultaneously rather than segmented versions of the data 
                         separately or feature-by-feature. The conceptual and mathematical 
                         simplicity of the approach, which pivotal step is spatial 
                         normalisation, involves the same operations irrespective of the 
                         complexity of the experiment or nature of the data, giving 
                         multivariate results that are easy to interpret. To demonstrate 
                         its performance we present experimental results on artificially 
                         generated data set and real medical data.",
  conference-location = "Manaus, AM, Brazil",
      conference-year = "8-11 Oct. 2006",
                  doi = "10.1109/SIBGRAPI.2006.19",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2006.19",
             language = "en",
                  ibi = "6qtX3pFwXQZG2LgkFdY/LFJ24",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/LFJ24",
           targetfile = "thomaz-multivariate.pdf",
        urlaccessdate = "2024, Apr. 27"
}