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This article presents a method for decomposing a sequence of spectroscopic signals into a sum of peaks whose centers, amplitudes and widths are estimated. Since the peaks exhibit a slow evolution through the sequence, the decomposition is performed jointly on every spectra. To this end, we have developed a Bayesian model where a Markov random field favors a smooth evolution of the peaks through the...
This article presents a method for decomposing a temporal sequence of photoelectron spectra into a parameter set reflecting the positions, amplitudes, and widths of the peaks. Since the peaks exhibit a slow evolution with time, we propose to take into account this temporal information by jointly decomposing the whole sequence. To this end, we have developed a Bayesian model where a Gaussian Markov...
Most brain functional connectivity methods in fMRI require a brain parcellation into functionally homogeneous regions. In this work we propose a novel parcellation approach based on a spatial hierarchical clustering, that provides clusters within a multi-level framework. The method has the advantage of producing several brain parcellations rather than a single one from a fixed size-homogeneity criterion...
We propose a new generic framework for segmentation of 3D digital data, based on knowledge contained in a segmentation example of similar data. The integration of prior knowledge is made by registering the image to segment on the segmentation example. Since the registration step relies on binary segmented data, segmentation and registration are performed jointly in a coarse-to-fine way using a multiscale...
Estimating significant changes between two images remains a challenging problem in medical image processing. This paper proposes a non-parametric region based method to detect significant changes in 3D multimodal Magnetic Resonance (MR) sequences. The proposed approach relies on an a contrario model which defines significant changes as events with very low probability. We adapt the a contrario framework...
This paper presents a novel, completely unsupervised fMRI brain mapping method that addresses the three problems of hemodynamic response function (HRF) variability, hemodynamic event timing, and fMRI response non-linearity. Spatial and temporal information are directly taken into account into the core of the activation detection process. In practice, activation detection at voxel v is formulated in...
This paper presents a novel statistical approach for the modeling and analysis of structured random processes observed through multiple event sequences: the hidden Markov multiple event sequence model (HMMESM). This model accounts for several features of these processes: (i) the hidden-observable aspect of the event sequences to be analyzed, (ii) the multiplicity of the observed event sequences, (iii)...
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