Christian Heinrich

Hubs of brain functional networks are radically reorganized in comatose patients.

Human brain networks have topological properties in common with many other complex systems, prompting the following question: what aspects of brain network organization are critical for distinctive functional properties of the brain, such as consciousness? To address this question, we used graph theoretical methods to explore brain network topology in resting state functional MRI data acquired from 17 patients with severely impaired consciousness and 20 healthy volunteers. We found that many global network properties were conserved in comatose patients. Specifically, there was no significant abnormality of global efficiency, clustering, small-worldness, modularity, or degree distribution in the patient group. However, in every patient, we found evidence for a radical reorganization of high degree or highly efficient “hub” nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of comatose brain networks and vice versa. These results indicate that global topological properties of complex brain networks may be homeostatically conserved under extremely different clinical conditions and that consciousness likely depends on the anatomical location of hub nodes in human brain networks.

3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization

This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of , which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis. Topology preservation is enforced by controlling the Jacobian of the transformation. Finding the optimal displacement parameters amounts to solving a constrained optimization problem: The residual energy between the target image and the deformed source image is minimized under constraints on the Jacobian. Unlike the 2-D case, in which simple linear constraints are derived, the 3-D B-spline-based deformable mapping yields a difficult (until now, unsolved) optimization problem. In this paper, we tackle the problem by resorting to interval analysis optimization techniques. Care is taken to keep the computational burden as low as possible. Results on multipatient 3-D MRI registration illustrate the ability of the method to preserve topology on the continuous image domain.

Retrospective evaluation of a topology preserving non-rigid registration method

This paper proposes a comprehensive evaluation of a monomodal B-spline-based non-rigid registration algorithm allowing topology preservation in 3-D. This article is to be considered as the companion of [Noblet, V., Heinrich, C., Heitz, F., Armspach, J.-P., 2005. 3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization. IEEE Transactions on Image Processing, 14 (5), 553-566] where this algorithm, based on the minimization of an objective function, was introduced and detailed. Overall assessment is based on the estimation of synthetic deformation fields, on average brain construction, on atlas-based segmentation and on landmark mapping. The influence of the model parameters is characterized. Comparison between several objective functions is carried out and impact of their symmetrization is pointed out. An original intensity normalization scheme is also introduced, leading to significant improvements of the registration quality. The comparison benchmark is the popular demons algorithm [Thirion, J.-P., 1998. Image matching as a diffusion process: an analogy with Maxwells demons. Medical Image Analysis, 2 (3), 243-260], that exhibited best results in a recent comparison between several non-rigid 3-D registration methods [Hellier, P., Barillot, C., Corouge, I., Gibaud, B., Le Goualher, G., Collins, D.L., Evans, A., Malandain, G., Ayache, N., Christensen, G.E., Johnson, H.J., 2003. Retrospective evaluation of intersubject brain registration. IEEE Transactions on Medical Imaging, 22 (9), 1120-1130]. The topology preserving B-spline-based method proved to outperform the commonly available ITK implementation of the demons algorithms on many points. Some limits of intensity-based registration methods are also highlighted through this work.

A Non-Local Fuzzy Segmentation Method: Application to Brain MRI

The Fuzzy C-Means algorithm is a widely used and flexible approach for brain tissue segmentation from 3D MRI. Despite its recent enrichment by addition of a spatial dependency to its formulation, it remains quite sensitive to noise. In order to improve its reliability in noisy contexts, we propose a way to select the most suitable example regions for regularisation. This approach inspired by the Non-Local Mean strategy used in image restoration is based on the computation of weights modelling the grey-level similarity between the neighbourhoods being compared. Experiments were performed on MRI data and results illustrate the usefulness of the approach in the context of brain tissue classification.

Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation

Spatially varying mixture models are characterized by the dependence of their mixing proportions on location (contextual mixing proportions) and they have been widely used in image segmentation. In this work, Gauss-Markov random field (MRF) priors are employed along with spatially varying mixture models to ensure the preservation of region boundaries in image segmentation. To preserve region boundaries, two distinct models for a line process involved in the MRF prior are proposed. The first model considers edge preservation by imposing a Bernoulli prior on the normally distributed local differences of the contextual mixing proportions. It is a discrete line process model whose parameters are computed by variational inference. The second model imposes Gamma prior on the Student’s-t distributed local differences of the contextual mixing proportions. It is a continuous line process whose parameters are also automatically estimated by the Expectation-Maximization (EM) algorithm. The proposed models are numerically evaluated and two important issues in image segmentation by mixture models are also investigated and discussed: the constraints to be imposed on the contextual mixing proportions to be probability vectors and the MRF optimization strategy in the frameworks of the standard and variational EM algorithm.

Digestive Activity Evaluation by Multichannel Abdominal Sounds Analysis

This paper introduces a complete methodology for abdominal sounds analysis, from signal acquisition to statistical data analysis. The goal is to evaluate if and how phonoenterograms can be used to detect different functioning modes of the normal gastrointestinal tract, both in terms of localization and of time evolution during the digestion. After the description of the acquisition protocol and the employed instrumentation, several signal processing steps are presented: wavelet denoising and segmentation, artifact suppression, and source localization. Next, several physiological features are extracted from the processed signals issued from a database of 14 healthy volunteers, recorded during 3 h after a standardized meal. Data analysis is performed using a multifactorial statistical method. Based on the introduced approach, we show that the abdominal regions of healthy volunteers present statistically significant phonoenterographic characteristics, which evolve differently during the normal digestion. The most significant feature allowing us to distinguish regions and time differences is the number of recorded sounds, but important information is also carried by sound amplitudes, frequencies, and durations. Depending on the considered feature, the sounds produced by different abdominal regions (especially stomach, ileocaecal, and lower abdomen regions) present a specific distribution over space and time. This information, statistically validated, is usable in further studies as a comparison term with other normal or pathological conditions.

Polarimetric data reduction: a Bayesian approach

In this paper, we introduce a general Bayesian approach to estimate polarization parameters in the Stokes imaging framework. We demonstrate that this new approach yields a neat solution to the polarimetric data reduction problem that preserves the physical admissibility constraints and provides a robust clustering of Stokes images in regard to image noises. The proposed approach is extensively evaluated by using synthetic simulated data and applied to cluster and retrieves the Stokes image issuing from a set of real measurements.

Iterative wavelet-based denoising methods and robust outlier detection

The goal of this letter is to study convergence conditions for a previously presented iterative wavelet denoising method and to shed light on its relationship with outlier rejection. This method involves a user-defined parameter, which must fulfill certain conditions in order to ensure denoising. Using generalized Gaussian modeling for the wavelet coefficients distribution, we obtain a lower bound for this parameter, and the resulting threshold, both adapted to the shape of the distribution. The properties of this threshold are examined, and the proposed method is compared with other classical rejection methods.

Symmetric Nonrigid Image Registration: Application to Average Brain Templates Construction

Image registration aims at estimating a consistent mapping between two images. Common techniques consist in choosing arbitrarily one image as a reference image and the other one as a floating image, thus leading to the estimation of inconsistent mappings. We present a symmetric formulation of the registration problem that maps the two images in a common coordinate system halfway between them. This framework has been considered to devise an efficient strategy for mapping a large set of images in a common coordinate system. Some results are presented in the context of 3-D nonrigid brain MR image registration for the construction of average brain templates.

A Topology Preserving Non-rigid Registration Method Using a Symmetric Similarity Function-Application to 3-D Brain Images

3-D non-rigid brain image registration aims at estimating consistently long-distance and highly nonlinear deformations corresponding to anatomical variability between individuals. A consistent mapping is expected to preserve the integrity of warped structures and not to be dependent on the arbitrary choice of a reference image: the estimated transformation from A to B should be equal to the inverse transformation from B to A. This paper addresses these two issues in the context of a hierarchical parametric modeling of the mapping, based on B-spline functions. The parameters of the model are estimated by minimizing a symmetric form of the standard sum of squared differences criterion. Topology preservation is ensured by constraining the Jacobian of the transformation to remain positive on the whole continuous domain of the image as a non trivial 3-D extension of a previous work [1] dealing with the 2-D case. Results on synthetic and real-world data are shown to illustrate the contribution of preserving topology and using a symmetric similarity function.