A fast Dejittering approach for line scanning microscopy

International audienceWe propose two efficient optimization approaches to correct jitter effects appearing in a specific type of line scanning microscopy. In this modality, even lines suffer from a non uniform and non integer distortion with respect to odd lines, creating significant visual artifacts. The huge image size make this problem highly challenging. To handle it, we propose two techniques. One is based on dynamic programming and has a complexity linear w.r.t. the number of pixels. The second is based on a convex relaxation and can be particularly efficient for parallel architectures. Both algorithms provide globally optimal solutions. The empirical reconstruction results are of high quality

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

Absolute localisations in indoor environment based on omnidirectionnal vision

In this article, we present two localization methods based on the omnidirectional SYCLOP sensor. The first method is a static one and is based on a matching between one real image and a base of synthetics panoramic pictures. This base is computed with the help of a modelling and a calibration of the SYCLOP sensor. The second method is a dynamic one and is based on multi target tracking. The originality of this method is its capability to manage and propagate uncertainties during the localization process. This multi-level uncertainty propagation stage is based on the use of the Dempster-Shafer theory. In this work, SYCLOP is associated with a panoramic range finder. It enables to treat complementary and redundant data and thus to construct a robust sensorial model which integrates an important number of significant primitives. Based on this model, we treat the problem of maintaining a matching and propagating uncertainties on each matched primitive in order to obtain a global uncertainty about the robot configuration.Dans cet article, nous présentons deux méthodes de localisation basées sur l'utilisation du capteur omnidirectionnel SYCLOP. La première méthode, statique, consiste à rechercher la configuration du robot d'une manière absolue par la mise en correspondance d'une image réelle avec une base d'images panoramiques synthétiques. Cette base est obtenue grâce à la modélisation et la calibration du capteur SYCLOP. La deuxième méthode, dynamique, est basée sur de la poursuite multi-cibles. L'originalité de cette méthode réside dans sa capacité à gérer et propager des incertitudes durant le processus de localisation. Cette propagation multi-niveaux d'incertitudes est basée sur l'utilisation de la théorie de Dempster Shafer. Dans ce travail, SYCLOP est associé à un capteur de profondeur. Ceci permet de traiter des données à la fois complémentaires et redondantes et donc de construire un modèle sensoriel robuste intégrant un nombre important de primitives significatives. En nous appuyant sur ce modèle, nous traitons le problème de maintien d'un appariement et de la propagation d'une incertitude sur chaque primitive appariée, ceci dans le but d'obtenir une incertitude globale qui caractérise l'estimation de configuration du robot

Localization method based on omnidirectional stereoscopic vision and dead-reckoning

and dead-reckonin

A fast Dejittering approach for line scanning microscopy

International audienceWe propose two efficient optimization approaches to correct jitter effects appearing in a specific type of line scanning microscopy. In this modality, even lines suffer from a non uniform and non integer distortion with respect to odd lines, creating significant visual artifacts. The huge image size make this problem highly challenging. To handle it, we propose two techniques. One is based on dynamic programming and has a complexity linear w.r.t. the number of pixels. The second is based on a convex relaxation and can be particularly efficient for parallel architectures. Both algorithms provide globally optimal solutions. The empirical reconstruction results are of high quality

A fast Dejittering approach for line scanning microscopy

International audienceWe propose two efficient optimization approaches to correct jitter effects appearing in a specific type of line scanning microscopy. In this modality, even lines suffer from a non uniform and non integer distortion with respect to odd lines, creating significant visual artifacts. The huge image size make this problem highly challenging. To handle it, we propose two techniques. One is based on dynamic programming and has a complexity linear w.r.t. the number of pixels. The second is based on a convex relaxation and can be particularly efficient for parallel architectures. Both algorithms provide globally optimal solutions. The empirical reconstruction results are of high quality

A fast Dejittering approach for line scanning microscopy

International audienceWe propose two efficient optimization approaches to correct jitter effects appearing in a specific type of line scanning microscopy. In this modality, even lines suffer from a non uniform and non integer distortion with respect to odd lines, creating significant visual artifacts. The huge image size make this problem highly challenging. To handle it, we propose two techniques. One is based on dynamic programming and has a complexity linear w.r.t. the number of pixels. The second is based on a convex relaxation and can be particularly efficient for parallel architectures. Both algorithms provide globally optimal solutions. The empirical reconstruction results are of high quality