Biomedical subjects

P Hellier

Publications and source records attributed to P Hellier.

4 recordsLinked to original sources

PubMed2003

Interindividual functional mapping: a nonlinear local approach.

Within the scope of three-dimensional brain imaging, we propose an interindividual fusion scheme to register functional activations according to anatomical cortical structures, the sulci. This paper is based on the assumption that an important part of functional intersubject variability is encoded in anatomical variability. The aim of this paper is therefore to propose a generic framework to register functional activations according to the relevant anatomical landmarks. Compared to "classical" interindividual fusion schemes, this approach is local. It relies on a statistical sulci shape model accounting for the interindividual variability of a population of subjects and providing deformation modes relative to a reference shape (a mean sulcus). The deformation field obtained between a given sulcus and the reference sulcus is extended to a neighborhood of the given sulcus by using the thin-plate spline interpolation. It is then applied to functional activations located in the vicinity of this sulcus. This approach is compared with rigid and nonrigid registration methods. In this paper, we present results on MEG somatosensory data acquired on 18 subjects. We show that the nonlinear local fusion scheme significantly reduces the observed functional variability.

Adult↗

PubMed2003

Retrospective evaluation of intersubject brain registration.

Although numerous methods to register brains of different individuals have been proposed, no work has been done, as far as we know, to evaluate and objectively compare the performances of different nonrigid (or elastic) registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the relevance of the registration. We have chosen to focus more particularly on the matching of cortical areas, since intersubject registration methods are dedicated to anatomical and functional normalization, and also because other groups have shown the relevance of such registration methods for deep brain structures. Experiments were conducted using 6 methods on a database of 18 subjects. The global measures used show that the quality of the registration is directly related to the transformation's degrees of freedom. More surprisingly, local measures based on the matching of cortical sulci did not show significant differences between rigid and non rigid methods.

Adult↗

PubMed2001

Segmentation of brain 3D MR images using level sets and dense registration.

This paper presents a strategy for the segmentation of brain from volumetric MR images which integrates 3D segmentation and 3D registration processes. The segmentation process is based on the level set formalism. A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. In this work, the propagation relies on a robust evolution model including adaptive parameters. These depend on the input data and on statistical distribution models. The main contribution of this paper is the use of an automatic registration method to initialize the surface, as an alternative solution to manual initialization. The registration is achieved through a robust multiresolution and multigrid minimization scheme. This coupling significantly improves the quality of the method, since the segmentation is faster, more reliable and fully automatic. Quantitative and qualitative results on both synthetic and real volumetric brain MR images are presented and discussed.

Algorithms↗

PubMed2001

Hierarchical estimation of a dense deformation field for 3-D robust registration.

A new method for medical image registration is formulated as a minimization problem involving robust estimators. We propose an efficient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to refine the estimation in specified areas. At each stage of the hierarchical estimation, we refine current estimate by seeking a piecewise affine model for the incremental deformation field. The performance of this method is numerically evaluated on simulated data and its benefits and robustness are shown on a database of 18 magnetic resonance imaging scans of the head.

Computer Simulation↗