International audienceAutomatically stabilizing moving living cells in fluorescence microscopy image sequences is required to attain and analyze the actual displacements of subcellular particles. We have designed a stabilization method which can handle within a single parametric framework, the estimation of the global motion and of the temporal intensity variation (e.g., due to photobleaching effect) that we have to compensate for. We have introduced extended parametric motion-intensity constraints and exploited a robust multiresolution estimation scheme insensitive to local independent motions (outliers). We demonstrate the efficiency and the accuracy of our stabilization method on three challenging cellular events: cell development, endosome displacements, protein recruitment

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This paper falls within the scope of joint segmentation-registration using nonlinear elasticity principles. Because Saint Venant-Kirchhoff materials are the simplest hyperelastic materials (hyperelasticity being a suitable framework when dealing with large and nonlinear deformations), we propose viewing the shapes to be matched as such materials. Then we introduce a variational model combining a measure of dissimilarity based on weighted total variation and a regularizer based on the stored energy function of a Saint Venant-Kirchhoff material. Adding a weighted total variationbased criterion enables us to align the edges of the objects even when the modalities are different. We derive a relaxed problem associated to the initial one for which we are able to provide a result of existence of minimizers. A description and analysis of a numerical method of resolution based on a decoupling principle is then provided including a theoretical result of Γ-convergence. Applications are illustrated in academic and biological images.

In this paper, we address the issue of designing a theoretically well-motivated and computationally efficient method ensuring topology preservation on image-registration-related deformation fields. The model is motivated by a mathematical characterization of topology preservation for a deformation field mapping two subsets of Z 2 , namely, positivity of the four approximations to the Jacobian determinant of the deformation on a square patch. The first step of the proposed algorithm thus consists in correcting the gradient vector field of the deformation (that does not comply with the topology preservation criteria) at the discrete level in order to fulfill this positivity condition. Once this step is achieved, it thus remains to reconstruct the deformation field, given its full set of discrete gradient vectors. We propose to decompose the reconstruction problem into independent problems of smaller dimensions, yielding a natural parallelization of the computations and enabling us to reduce drastically the computational time (up to 80 in some applications). For each subdomain, a functional minimization problem under Lagrange interpolation constraints is introduced and its well-posedness is studied: existence/uniqueness of the solution, characterization of the solution, convergence of the method when the number of data increases to infinity, discretization with the Finite Element Method and discussion on the properties of the matrix involved in the linear system. Numerical simulations based on OpenMP parallelization and MKL multi-threading demonstrating the ability of the model to handle large deformations (contrary to classical methods) and the interest of having decomposed the problem into smaller ones are provided.