ROC performance evaluation of RADSPM technique

The purpose of Functional Magnetic Resonance Imaging (fMRI) is to map areas of increased neuronal activity of the human brain. fMRI has been applied to investigate a variety of neuronal processes from activities in the primary sensory and motor cortices to cognitive functions such as perception or l...

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Detalles Bibliográficos
Autores principales: Giacomantone, Javier, De Giusti, Armando Eduardo
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2008
Materias:
Ciencias Informáticas
Functional Magnetic Resonance Imaging
fMRI classification
ROC curve
functional image processing
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/21773
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-21773
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Functional Magnetic Resonance Imaging
fMRI classification
ROC curve
functional image processing
spellingShingle Ciencias Informáticas
Functional Magnetic Resonance Imaging
fMRI classification
ROC curve
functional image processing
Giacomantone, Javier
De Giusti, Armando Eduardo
ROC performance evaluation of RADSPM technique
topic_facet Ciencias Informáticas
Functional Magnetic Resonance Imaging
fMRI classification
ROC curve
functional image processing
description The purpose of Functional Magnetic Resonance Imaging (fMRI) is to map areas of increased neuronal activity of the human brain. fMRI has been applied to investigate a variety of neuronal processes from activities in the primary sensory and motor cortices to cognitive functions such as perception or learning. Robust anisotropic diffusion of statistical parametric maps (RADSPM) is a new technique to improve functional Magnetic Resonance Imaging. RADSPM attempts to improve voxel classification based on robust anisotropic diffusion (RAD) to include the spatial relationship between active voxels. This paper compares two fMRI postprocessing techniques used to identify areas of increased neuronal activity, a widely used method, correlation analysis, and RADSPM. In recent years, the use of ROC analysis has been extended from its original use in communication systems to machine learning, pattern classification and fMRI. We proposed to use ROC curves and the area under the curve (AUC) not only as a final performance evaluation and visualizing technique but as a gauging parameter procedure in RADSPM. We give a brief review of the main methods and conclude presenting experimental results and suggesting further research alternatives.
format Objeto de conferencia
Objeto de conferencia
author Giacomantone, Javier
De Giusti, Armando Eduardo
author_facet Giacomantone, Javier
De Giusti, Armando Eduardo
author_sort Giacomantone, Javier
title ROC performance evaluation of RADSPM technique
title_short ROC performance evaluation of RADSPM technique
title_full ROC performance evaluation of RADSPM technique
title_fullStr ROC performance evaluation of RADSPM technique
title_full_unstemmed ROC performance evaluation of RADSPM technique
title_sort roc performance evaluation of radspm technique
publishDate 2008
url http://sedici.unlp.edu.ar/handle/10915/21773
work_keys_str_mv AT giacomantonejavier rocperformanceevaluationofradspmtechnique
AT degiustiarmandoeduardo rocperformanceevaluationofradspmtechnique
bdutipo_str Repositorios
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