Sylvain Faisan

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

Description des activités

Polarimetric Image Processing

We have proposed a new procedure to estimate polarization signatures for Stokes or Mueller images, while preserving sharp transitions. The procedure is based on non-local means (NLM) filtering, which is an efficient denoising algorithm that outperforms popular denoising methods regarding the preservation of sharp edges and fine texture details. The noise is filtered while yielding physically admissible Stokes vectors (or Mueller matrices) at each pixel location. The proposed joint filtering-estimation procedure is expressed as a constrained optimization problem. Interestingly, we show that it can be equivalently seen as a two step method: a filtering stage based on the NLM approach followed by an estimation step ensuring physical admissibility. Ellipticity of the Stokes vectors estimated with the proposed approach (left) and by using the pseudo-inverse (right) are presented.

S. Faisan, C. Heinrich, G. Sfikas, J. Zallat, Estimation of Mueller matrices using non-local means filtering . Optics Express, pp. 4424--4438, Vol. 21, Num. 4, doi:10.1364/OE.21.004424, February 2013
S. Faisan, C. Heinrich, F. Rousseau, A. Lallement, J. Zallat, Joint filtering-estimation of Stokes vector images based on a non-local means approach . Journal of the Optical Society of America. A, Optics, Image Science, and Vision, pp. 2028--2037, Vol. 29, Num. 9, doi:10.1364/JOSAA.29.002028, September 2012

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Decomposition of Spectroscopic signal sequences

Retinal Image Registration

We have proposed a new, robust and automated method for registering sequences of images acquired from scanning ophthalmoscopes. The method uses a multi-scale B-spline representation of the deformation field to map images to each other and an hierarchical optimization method. We applied the method to video sequences acquired from different parts of the retina. In all cases, the registration was successful, even in the presence of large distortions from microsaccades, and the resulting deformation fields describe the fixational motion of the eye.

S. Faisan, D. Lara, C. Paterson, Scanning ophthalmoscope retinal image registration using one-dimensional deformation fields . Optics Express, pp. 4157--4169, Vol. 19, Num. 5, doi:10.1364/OE.19.004157, February 2011

Skull Image Analysis

functional MRI -- Brain Connectivity analysis

To do

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.

Publications

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