@InProceedings{Machado:2006:InStPo,
author = "Machado, Alexei Manso Correa",
affiliation = "{PUC Minas}",
title = "Increasing statistical power in medical image analysis",
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 = "multiple comparison correction, image registration, Bonferroni
correction.",
abstract = "In this paper, we present a novel method for estimating the
effective number of independent variables in imaging applications
that require multiple hypothesis testing. The method increases the
statistical power of the results by refuting the assumption of
independence among variables, while keeping the probability of
false positives low. It is based on the spectral graph theory, in
which the variables are seen as the vertices of a complete
undirected graph and the correlation matrix as the adjacency
matrix that weights its edges. By computing the eigenvalues of the
correlation matrix, it is possible to obtain valuable information
about the dependence levels among the variables of the problem.
The method is compared to other available models and its
effectiveness illustrated in a case study on the morphology of the
human corpus callosum. .",
conference-location = "Manaus, AM, Brazil",
conference-year = "8-11 Oct. 2006",
doi = "10.1109/SIBGRAPI.2006.27",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2006.27",
language = "en",
ibi = "6qtX3pFwXQZG2LgkFdY/LJyAe",
url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/LJyAe",
targetfile = "machado-increasing.pdf",
urlaccessdate = "2025, Feb. 16"
}