References of "Ramirez, J"
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See detailCombining feature extraction methods to assist the diagnosis of Alzheimer's disease
Segovia, Fermin; Górriz, J. M.; Ramírez, J. et al

in Current Alzheimer Research (2016), 13

Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the ... [more ▼]

Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database). [less ▲]

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See detailMuscular forces affecting the outcome of CCL surgery
Ramirez, J.; Böhme, Béatrice ULg; Vroomen, Carl ULg et al

in Proceedings of the 20th ECVS Congress (2011)

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See detailLong-term magnetic field monitoring of the sun-like star ξ Bootis A
Morgenthaler, A.; Petit, P.; Aurière, M. et al

in Boissier, S.; Heydari-Malayeri, M.; Samadi, R. (Eds.) et al SF2A-2010: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics (2010, December 01)

Phase-resolved observations of the solar-type star ξ Bootis A were obtained using the Narval spectropolarimeter at Telescope Bernard Lyot (Pic du Midi, France) during years 2007, 2008, 2009 and 2010. The ... [more ▼]

Phase-resolved observations of the solar-type star ξ Bootis A were obtained using the Narval spectropolarimeter at Telescope Bernard Lyot (Pic du Midi, France) during years 2007, 2008, 2009 and 2010. The data sets enable us to study both the rotational and the long-term evolution of various activity tracers. Here, we focus on the large-scale photospheric magnetic field (reconstructed by Zeeman-Doppler Imaging), the Zeeman broadening of the FeI 846.84 nm magnetic line, and the chromospheric CaII H and Hα emission. [less ▲]

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