Discriminant random field and patch-based redundancy analysis for image change detection

International audienceTo develop better image change detection algorithms, new models able to capture all the spatio-temporal regularities and geometries seen in an image pair are needed. In con- trast to the usual pixel-wise methods, we propose a patch- based formulation for modeling semi-local interactions and detecting occlusions and other local or regional changes in an image pair. To this end, the image redundancy property is exploited to detect unusual spatio-temporal patterns in the scene. We first define adaptive detectors of changes between two given image patches and combine locally in space and scale such detectors. The resulting score at a given loca- tion is exploited within a discriminant Markov random field (DRF) whose global optimization flags out changes with no optical flow computation. Experimental results on several applications demonstrate that the method performs well at detecting occlusions and meaningful regional changes and is especially robust in the case of low signal-to-noise ratios

Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs

SIAM Journal Multiscale Modeling & SimulationTo develop better image change detection algorithms, new models able to capture spatio-temporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semi-local inter-image interactions and detecting local or regional changes in an image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections obtained from the neighboring pixels, for different patch sizes. We study the statistical properties of the non-parametric detection approach that guarantees small probabilities of false alarms. Experimental results on several applications demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting occlusions and meaningful changes for a variety of illumination conditions and signal-to-noise ratios. The number of control parameters of the algorithm is small and the adjustment is intuitive in most cases

Modélisation et estimation du trafic intracellulaire par tomographie de réseaux et microscopie de fluorescence

This thesis presents a new method for analyzing and simulating vesicular trafficking in fluorescence video-microscopy. Instead of tracking each individual vesicle, we have developed a global approach (network tomography) that is inspired from previous works on road traffic analysis and network telecom- munication traffic analysis. This approach makes use of local countings of vesicles and a routing proce- dure to recover the global trajectories of vesicles on a whole image sequence. Contrary to the previous applications of network tomography, the local countings and the routing are also unknown in our case. In order to measure local countings of vesicles, we have developed a method for object and background estimation in fluorescence video-microscopy. This method exploits a non local detection term based on the similarity between image patches and considers the estimated background component as a reference to improve the detection. The routing procedure depends on vesicle countings for the traffic analysis, and is controlled by the user for the simulations. The generated synthetic image sequences enabled to evaluate quantitatively the vesicular trafficking estimation method. This method was also tested on real image sequences in the context of a study on the membranar transport and vesicular trafficking regulated by Rab6 isoforms.Cette thèse traite de l'analyse et de la simulation du trafic vésiculaire sur des séquences d'images de microscopie de fluorescence. À contre-courant des approches habituelles exploitant un suivi individuel des vésicules, nous avons développé une approche globale (tomographie de réseaux) inspirée de travaux antérieurs sur l'analyse du trafic routier et l'analyse du trafic sur des réseaux de télécommunications. Cette approche repose sur l'utilisation de comptages locaux de vésicules couplés à une procédure de routage qui permettent d'estimer les trajectoires globales des vésicules sur l'ensemble d'une séquence d'images. Contrairement aux précédentes applications de la tomographie de réseaux, les comptages et le routage sont également des inconnues du problème. Afin de mesurer les comptages locaux de vésicules, nous avons développé une méthode de séparation des composantes “objet” et “fond” dans des séquences de microscopie de fluorescence. Cette méthode exploite un terme de détection non local reposant sur la similarité entre motifs de l'image et utilise la composante “fond” estimée comme “référence” pour améliorer la détection des vésicules. Par ailleurs, la procédure de routage dépend des données observées. Dans le cas de l'estimation du trafic, le routage est établi à partir du comptage des vésicules ; dans le cas de simulations, le routage est contrôlé par l'utilisateur. La génération de séquences synthétiques a permis d'évaluer quantitativement la méthode d'estimation du trafic vésiculaire. Cette méthode a égale- ment été évaluée sur des séquences d'images réelles de microscopie dans le cadre d'une étude précise sur le transport membranaire et le trafic vésiculaire régulé par des isoformes de la protéine Rab6

Patch-based Markov models for change detection in image sequence analysis

International audienceChange detection between two images is challenging and needed in a wide variety of imaging applications. Several approaches have been yet developed, especially methods based on difference image. In this paper, we propose an original patch-based Markov modeling framework to detect spatial irregularities in the difference image with low false alarm rates. Experimental results show that the proposed approach performs well for change detection, especially for images with low signal-to-noise ratios

Minimal paths and probabilistic models for origin-destination traffic estimation in live cell imaging

International audienceGreen Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells. Original image analysis methods are then required to process challenging 2D or 3D image sequences. To address the tracking problem of several hundreds of objects, we propose an original framework that provides general information about vesicle transport, that is traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the advances in Network Tomography commonly used in network communications. In this paper, we address image partition given vesicle stocking areas and multipaths routing for vesicle transport. This approach has been developed for real fluorescence image sequences and Rab proteins

Network Tomography-based tracking for intracellular traffic analysis in fluorescence microscopy imaging

International audienceDetermination of the sub-cellular localization and dynamics of any proteins is an important step towards the understanding of multi-molecular complexes in a cellular context. Green Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy allows to acquire multidimensional data on rapid cellular activities, and then make possible the analysis of proteins of interest. Consequently, novel techniques of image analysis are needed to quantify dynamics of biological processes observed in such image sequences. In biological trafficking analysis, the previous tracking methods do not manage when many small and poorly distinguishable objects interact. Nevertheless, an another way of tracking that usually consists in determining the full trajectories of all the objects, can be more relevant. General information about the traffic like the regions of origin and destination of the moving objects represent interesting features for analysis. In this paper, we propose to estimate the paths (regions of origin and destination) used by the objects of interest, and the proportions of moving objects for each path. This can be accomplished by exploiting the recent advances in Network Tomography (NT) commonly used in network communications. This idea is demonstrated on real image sequences for the Rab6 protein, a GTPase involved in the regulation of intracellular membrane trafficking

Minimal paths and probabilistic models for origin-destination traffic estimation in live cell imaging

International audienceGreen Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells. Original image analysis methods are then required to process challenging 2D or 3D image sequences. To address the tracking problem of several hundreds of objects, we propose an original framework that provides general information about vesicle transport, that is traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the advances in Network Tomography commonly used in network communications. In this paper, we address image partition given vesicle stocking areas and multipaths routing for vesicle transport. This approach has been developed for real fluorescence image sequences and Rab proteins

Discriminant random field and patch-based redundancy analysis for image change detection

International audienceTo develop better image change detection algorithms, new models able to capture all the spatio-temporal regularities and geometries seen in an image pair are needed. In con- trast to the usual pixel-wise methods, we propose a patch- based formulation for modeling semi-local interactions and detecting occlusions and other local or regional changes in an image pair. To this end, the image redundancy property is exploited to detect unusual spatio-temporal patterns in the scene. We first define adaptive detectors of changes between two given image patches and combine locally in space and scale such detectors. The resulting score at a given loca- tion is exploited within a discriminant Markov random field (DRF) whose global optimization flags out changes with no optical flow computation. Experimental results on several applications demonstrate that the method performs well at detecting occlusions and meaningful regional changes and is especially robust in the case of low signal-to-noise ratios

Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs

International audienceTo develop better image change detection algorithms, new models able to capture spatiotemporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semilocal interimage interactions and detecting local or regional changes in an image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections obtained from the neighboring pixels for different patch sizes. We study the statistical properties of the nonparametric detection approach that guarantees small probabilities of false alarms. Experimental results on several applications demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting occlusions and meaningful changes for a variety of illumination conditions and signal-to-noise ratios. The number of control parameters of the algorithm is small, and the adjustment is intuitive in most cases

Studying Muscle Transcriptional Dynamics at Single-molecule Scales in <em>Drosophila</em>

International audienceSkeletal muscles are large syncytia made up of many bundled myofibers that produce forces and enable body motion. Drosophila is a classical model to study muscle biology. The combination of both Drosophila genetics and advanced omics approaches led to the identification of key conserved molecules that regulate muscle morphogenesis and regeneration. However, the transcriptional dynamics of these molecules and the spatial distribution of their messenger RNA within the syncytia cannot be assessed by conventional methods. Here we optimized an existing singlemolecule RNA fluorescence in situ hybridization (smFISH) method to enable the detection and quantification of individual mRNA molecules within adult flight muscles and their muscle stem cells. As a proof of concept, we have analyzed the mRNA expression and distribution of two evolutionary conserved transcription factors, Mef2 and Zfh1/Zeb. We show that this method can efficiently detect and quantify single mRNA molecules for both transcripts in the muscle precursor cells, adult muscles, and muscle stem cells. © 2023 JoVE Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License