• Aucun résultat trouvé

Adaptive Mean Shift Based Hemodynamic Brain Parcellation in fMRI

N/A
N/A
Protected

Academic year: 2021

Partager "Adaptive Mean Shift Based Hemodynamic Brain Parcellation in fMRI"

Copied!
13
0
0

Texte intégral

Loading

Figure

Fig. 1: Reference parcellations (RP) used for the 4 experiments and corresponding estimated parcellation (EP) (grid size = 20 × 20).
Fig. 2: Reference activation labels (left column) and reference NRLs (right column) for the two experimental conditions (grid size = 20 × 20).
Table 2: Confusion matrices for Exps. 3 and 4. RP and EP refer to the reference and the estimated parcellations, respectively.
Fig. 4: HRF estimates for the synthetic data experiments.
+2

Références

Documents relatifs

Jameson Graber and Alain Bensoussan, Existence and uniqueness of solutions for Bertrand and Cournot mean field games, Applied Mathematics & Optimization (2016).

A robust method for addressing the problems associated with processing data obtained from multi-modality imaging is mean shift which is based on feature space analysis and

Moreover, model-free methods could be used for both activation detection in task study and functional networks analysis in resting-state study, which is difficult for model-based

In order to take these aspects into consideration and achieve the goals mentioned above, our approach combines in the OLED structure three types of optical

/ La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur. Access

The thickness of the thin film grown on top of the shadowing block is also plotted (as a dashed line). As the radial distance is increased the thickness grows until it reaches

We have analysed whether the inconsistency between the rate of the sea level rise at IJmuiden, Hoek van Holland, Maassluis and Vlissingen can be due to an abrupt shift of mean level

A sharp distinction of our approach compared to previous models is that we do not restrict ourselves to data for each state but ac- company them with a set of interesting