De Équipe IMAGeS : Images, Modélisation, Apprentissage, Géométrie et Statistique
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Maître de conférences
ICube - MIV 300 Bd Sébastien Brant BP 10413 67412 Illkirch CEDEX - France
Tel : + 33 (0) 3 68 85 44 89 Fax : + 33 (0) 3 68 85 44 97 Bureau : C211
Courriel : faisan(at)unistra(dot)fr
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Description des activités
Polarimetric Image Processing
To do
Decomposition of Spectroscopic signal sequences
To do
Retinal Image Registration
To do
Skull Image Analysis
functional MRI -- Brain Connectivity analysis
To do
vignette|test
Functional MRI -- Brain Mapping
Activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets (see Fig. a). The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs). It consists in hypothesizing about a set of valid scenarios that could explain the N observed HRO event sequences.
A valid scenario (see Fig. b), relies on the combination of: ||
- an N + 1th event sequence, namely, a candidate sequence of task-induced hemodynamic activation onsets (HAOs) at the origin of the observations.
- HAO signatures, that is, associations of HRO events across channels, each association corresponding to the observable counterpart of a single HAO event. By definition, a signature is composed of one HRO event by observation channel, the event being observed (black line) or not (black point) (see Fig. b).
- Causality constraints between signatures given an HAO sequence.
If you are interested, you can find more information in the two following articles. In the second article, the neighborhood of v is not considered (N=1).
- S. Faisan, L. Thoraval, J.-P. Armspach, F. Heitz, Hidden Markov multiple event sequence models : a paradigm for the spatio-temporal analysis of fMRI data . Medical Image Analysis, pp. 1--20, Vol. 11, Num. 1, doi:10.1016/j.media.2006.09.003, February 2007.
- S. Faisan, L. Thoraval, J.-P. Armspach, M.-N. Lutz, F. Heitz, Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models. IEEE Transactions on Medical Imaging, pp. 263-276, Vol. 24, Num. 2, doi:10.1109/TMI.2004.841225, February 2005.
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Publications
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