ATMAD : robust image analysis for Automatic Tissue MicroArray De-arraying

International audienceBackground. Over the last two decades, an innovative technology called Tissue Microarray (TMA),which combines multi-tissue and DNA microarray concepts, has been widely used in the field ofhistology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembledonto a single support – typically a glass slide – according to a design grid (array) layout, in order toallow multiplex analysis by treating numerous samples under identical and standardized conditions.However, during the TMA manufacturing process, the sample positions can be highly distorted fromthe design grid due to the imprecision when assembling tissue samples and the deformation of theembedding waxes. Consequently, these distortions may lead to severe errors of (histological) assayresults when the sample identities are mismatched between the design and its manufactured output.The development of a robust method for de-arraying TMA, which localizes and matches TMAsamples with their design grid, is therefore crucial to overcome the bottleneck of this prominenttechnology.Results. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD)approach dedicated to images acquired with bright field and fluorescence microscopes (or scanners).First, tissue samples are localized in the large image by applying a locally adaptive thresholdingon the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametricshape model is considered for segmenting ellipse-shaped objects at each detected position.Segmented objects that do not meet the size and the roundness criteria are discarded from thelist of tissue samples before being matched with the design grid. Sample matching is performed byestimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimateddeformation, the true tissue samples that were preliminary rejected in the early image processingstep are recognized by running a second segmentation step.Conclusions. We developed a novel de-arraying approach for TMA analysis. By combining waveletbaseddetection, active contour segmentation, and thin-plate spline interpolation, our approach isable to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background andnon-linear deformation of TMA grid. In addition, the deformation estimation produces quantitativeinformation to asset the manufacturing quality of TMAs

A variational method for dejittering large fluorescence line scanner images

International audienceWe propose a variational method dedicated to jitter correction of large fluorescence scanner images. Our method consists in minimizing a global energy functional to estimate a dense displacement field representing the spatially-varying jitter. The computational approach is based on a half-quadratic splitting of the energy functional, which decouples the realignment data term and the dedicated differential-based regularizer. The resulting problem amounts to alternatively solving two convex and nonconvex optimization subproblems with appropriate algorithms. Experimental results on artificial and large real fluorescence images demonstrate that our method is not only capable to handle large displacements but is also efficient in terms of subpixel precision without inducing additional intensity artifacts

Constrained Control of Uncertain, Time-varying Linear Discrete-Time Systems Subject to Bounded Disturbances

International audienceThe aim of this paper is twofold. In the first part, robust invariance for ellipsoidal sets with respect to uncertain and/or time-varying linear discrete-time systems with bounded additive disturbances is revisited. We provide an extension of an existing invariance condition. In the second part a novelrobust interpolation based control design involving several local unconstrained robust optimal controls is proposed. At each timeinstant a quadratic programming problem is solved on-line. Proofs of recursive feasibility and input-to-state stability are given

Constrained control of discrete-time linear periodic system

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Quasi Min-Max MPC Design for Constrained LPV Systems

6 pagesInternational audienceThis paper proposes a novel model predictive controller for polytopic linear parameter-varying (LPV) discrete-time systems with state and control constraints. It is assumed that the time-varying parameters are measured online, but their future behavior is uncertain and contained in a given polytope. The main idea is to use an interpolation technique to obtain a suitable terminal constrained set and suitable terminal cost together with one-step model predictive controller. At each time instant a quadratic programming problem is solved on-line. Proofs recursive feasibility and asymptotic stability are given

More efficient interpolating control

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Generalized Sparse Variation Regularization for Large Fluorescence Image Deconvolution

In this work, we generalize the sparse variation (SV) combining the total-variation (TV) and the L 1 regularization and introduce a novel family of convex and non-quadratic regularizers for fast deconvolution of large 2D fluorescence images. These regularizers are defined as mixed Lp-L 2 norms (p ≥ 1) which group image intensity and spatial differentials, computed at each pixel of the image. By coupling a regularization term of this family with a quadratic data fidelity term, we propose a fast and efficient deconvolution method by using the primal-dual (proximal) algorithms to minimize the corresponding energy functional. Experiment results on both 2D simulated and real fluorescence scanner images demonstrate the performance of our method in terms of restoration quality as well as computational time

Improved MPC design for constrained linear periodic systems

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Experimental Validation of a Nonlinear MPC Strategy for a Wave Energy Converter Prototype

International audienceOne of the major limitations to the development of advancedwave energy converters (WECs) control strategies are the associatedcomputational costs. For instance, model predictive control(MPC) strategies have the potential to obtain almost optimalperformance, provided that the imperfect power conversion inthe power take-off (PTO) system is correctly taken into accountin the optimization criterion and that the incoming wave forcecan be estimated and forecast. However, demanding computationalrequirements as well as the unresolved issue of wave forceestimation have so far prevented real-time implementation andvalidation of such MPC strategies. In this paper, we present thesuccessful experimental results obtained on a scaled-down prototypeof the well-known Wavestar machine. Performance comparisonsare provided for nonlinear MPC versus a reference PIcontroller

Antenne Notch miniature et reconfigurable à base de couches minces BST

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