@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 = "2025, Feb. 16"
}