Literatura académica sobre el tema "Imagerie computationnelle"
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Artículos de revistas sobre el tema "Imagerie computationnelle"
Sun, R., E. J. Limkin, L. Dercle, S. Reuzé, E. I. Zacharaki, C. Chargari, A. Schernberg et al. "Imagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie". Cancer/Radiothérapie 21, n.º 6-7 (octubre de 2017): 648–54. http://dx.doi.org/10.1016/j.canrad.2017.07.035.
Texto completoEFTHIMIADOU, Euphrosyne. "Imagerie cérébrale vs imagerie computationnelle : intégrer les neurosciences dans la numérisation de l’éducation". Analele Universității din Craiova, seria Psihologie-Pedagogie/Annals of the University of Craiova, Series Psychology- Pedagogy 45, n.º 2 supplement (20 de febrero de 2024): 7–20. http://dx.doi.org/10.52846/aucpp.2023.2suppl.01.
Texto completoSun, R. y E. Deutsch. "Imagerie médicale computationnelle (radiomique) : principes et potentiel en onco-pneumologie". Revue des Maladies Respiratoires Actualités 12, n.º 2 (octubre de 2020): 2S307–2S313. http://dx.doi.org/10.1016/s1877-1203(20)30111-7.
Texto completoTesis sobre el tema "Imagerie computationnelle"
Debarnot, Valentin. "Microscopie computationnelle". Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30156.
Texto completoThe contributions of this thesis are numerical and theoretical tools for the resolution of blind inverse problems in imaging. We first focus in the case where the observation operator is unknown (e.g. microscopy, astronomy, photography). A very popular approach consists in estimating this operator from an image containing point sources (microbeads or fluorescent proteins in microscopy, stars in astronomy). Such an observation provides a measure of the impulse response of the degradation operator at several points in the field of view. Processing this observation requires robust tools that can rapidly use the data. We propose a toolbox that estimates a degradation operator from an image containing point sources. The estimated operator has the property that at any location in the field of view, its impulse response is expressed as a linear combination of elementary estimated functions. This makes it possible to estimate spatially invariant (convolution) and variant (product-convolution expansion) operators. An important specificity of this toolbox is its high level of automation: only a small number of easily accessible parameters allows to cover a large majority of practical cases. The size of the point source (e.g. bead), the background and the noise are also taken in consideration in the estimation. This tool, coined PSF-estimator, comes in the form of a module for the Fiji software, and is based on a parallelized implementation in C++. The operators generated by an optical system are usually changing for each experiment, which ideally requires a calibration of the system before each acquisition. To overcome this, we propose to represent an optical system not by a single operator (e.g. convolution blur with a fixed kernel for different experiments), but by subspace of operators. This set allows to represent all the possible states of a microscope. We introduce a method for estimating such a subspace from a collection of low rank operators (such as those estimated by the toolbox PSF-Estimator). We show that under reasonable assumptions, this subspace is low-dimensional and consists of low rank elements. In a second step, we apply this process in microscopy on large fields of view and with spatially varying operators. This implementation is possible thanks to the use of additional methods to process real images (e.g. background, noise, discretization of the observation).The construction of an operator subspace is only one step in the resolution of blind inverse problems. It is then necessary to identify the degradation operator in this set from a single observed image. In this thesis, we provide a mathematical framework to this operator identification problem in the case where the original image is constituted of point sources. Theoretical conditions arise from this work, allowing a better understanding of the conditions under which this problem can be solved. We illustrate how this formal study allows the resolution of a blind deblurring problem on a microscopy example.[...]
Pizzolato, Marco. "IRM computationnelle de diffusion et de perfusion en imagerie cérébrale". Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4017/document.
Texto completoDiffusion and Perfusion Magnetic Resonance Imaging (dMRI & pMRI) represent two modalities that allow sensing important and different but complementary aspects of brain imaging. This thesis presents a theoretical and methodological investigation on the MRI modalities based on diffusion-weighted (DW) and dynamic susceptibility contrast (DSC) images. For both modalities, the contributions of the thesis are related to the development of new methods to improve and better exploit the quality of the obtained signals. With respect to contributions in diffusion MRI, the nature of the complex DW signal is investigated to explore a new potential contrast related to tissue microstructure. In addition, the complex signal is exploited to correct a bias induced by acquisition noise of DW images, thus improving the estimation of structural scalar metrics. With respect to contributions in perfusion MRI, the DSC signal processing is revisited in order to account for the bias due to bolus dispersion. This phenomenon prevents the correct estimation of perfusion metrics but, at the same time, can give important insights about the pathological condition of the brain tissue. The contributions of the thesis are presented within a theoretical and methodological framework, validated on both synthetical and real images
Tondo, Yoya Ariel Christopher. "Imagerie computationnelle active et passive à l’aide d’une cavité chaotique micro-ondes". Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S130/document.
Texto completoThe broad topic of the presented Ph.D focuses on active and passive microwave computational imaging. The use of a chaotic cavity as a compressive component is studied both theoretically (mathematical model, algorithmic resolution of the inverse problem) and experimentally. The underlying idea is to replace an array of antennas with a single reverberant cavity with an array of openings on the front panel that encodes the spatial information of a scene in the temporal response of the cavity. The reverberation of electromagnetic waves inside the cavity provides the degrees of freedom necessary to reconstruct an image of the scene. Thus it is possible to create a high-resolution image of a scene in real time from a single impulse response. Applications include security or imaging through walls. In this work, the design and characterization of an open chaotic cavity is performed. Using this device, active computational imaging is demonstrated to produce images of targets of various shapes. The number of degrees of freedom is further improved by changing the boundary conditions with the addition of commercial fluorescent lamps. The interaction of the waves with these plasma elements allows new cavity configurations to be created, thus improving image resolution. Compressive imaging is next applied to the passive detection and localization of natural thermal radiation from noise sources, based on the correlation of signals received over two channels. Finally, an innovative method of interferometric target imaging is presented. It is based on the reconstruction of the impulse response between two antennas from the microwave thermal noise emitted by a network of neon lamps. This work constitutes a step towards for future imaging systems
Filipis, Luiza. "Etude optique et computationnelle de la fonction des canaux ioniques neuronaux". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAY078.
Texto completoThe physiology of ion channels is a major topic of interest in modern neuroscience since the functioning of these molecules is the biophysical ground of electrical and chemical behaviour of neurons. Ion channels are diverse membrane proteins that allow the selective passage of ions across the lipid bilayer of cells. They are involved in a variety of fundamental physiological processes from electrical signal integration, action potential generation and propagation to cell growth and even apoptosis, while their dysfunction is the cause of several diseases. Ion channels have extensively studied using electrode methods, in particular the patch-clamp technique, but these approaches are limited in studying native channels during physiological activity in situ. In particular, electrodes give limited spatial information while it is recognised that the contribution of channels in all different processes is a function not only of their discrete biophysical properties but also of their distribution across the neurons surface at the different compartments. Optical techniques, in particular those involving fluorescence imaging, can overcome intrinsic limitations of electrode techniques as they allow to record electrical and ionic signals with high spatial and temporal resolution. Finally, the ability of optical techniques combined with neuronal modelling can potentially give pivotal information significantly advancing our understanding on how neurons work.The ambitious goal of my thesis was to progress in this direction by developing novel approaches to combine cutting-edge imaging techniques with modelling to extract ion currents and channel kinetics in specific neuronal regions. The body of this work was divided in three methodological pieces, each of them described in a dedicated chapter
Skitioui, Salah. "Développement de radars millimétriques innovants". Electronic Thesis or Diss., Limoges, 2024. http://www.theses.fr/2024LIMO0017.
Texto completoThis research is part of a CIFRE thesis aimed at developing technologies to simplify and reduce costs associated with a body scanners dedicated to security applications, while improving the refresh rate of reconstructed images. The fundamental objective is to devise an affordable real-time imaging system. Research efforts are focused on leveraging analog multiplexing techniques based on frequency diversity, integrated into an FMCW architecture, to overcome temporal limitations inherent in existing approaches. To this end, a prototype of a leaky reverberation cavity has been conceptualized, subjected to laboratory testing, and subsequently integrated into an industrial measurement bench. This accomplishment represents a significant advancement in the evolution of a real-time imaging system utilizing an analog multiplexing device
Duan, Liuyun. "Modélisation géométrique de scènes urbaines par imagerie satellitaire". Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4025.
Texto completoAutomatic city modeling from satellite imagery is one of the biggest challenges in urban reconstruction. The ultimate goal is to produce compact and accurate 3D city models that benefit many application fields such as urban planning, telecommunications and disaster management. Compared with aerial acquisition, satellite imagery provides appealing advantages such as low acquisition cost, worldwide coverage and high collection frequency. However, satellite context also imposes a set of technical constraints as a lower pixel resolution and a wider that challenge 3D city reconstruction. In this PhD thesis, we present a set of methodological tools for generating compact, semantically-aware and geometrically accurate 3D city models from stereo pairs of satellite images. The proposed pipeline relies on two key ingredients. First, geometry and semantics are retrieved simultaneously providing robust handling of occlusion areas and low image quality. Second, it operates at the scale of geometric atomic regions which allows the shape of urban objects to be well preserved, with a gain in scalability and efficiency. Images are first decomposed into convex polygons that capture geometric details via Voronoi diagram. Semantic classes, elevations, and 3D geometric shapes are then retrieved in a joint classification and reconstruction process operating on polygons. Experimental results on various cities around the world show the robustness, scalability and efficiency of the proposed approach
Domenech, Philippe. "Une approche neuro-computationnelle de la prise de décision et de sa régulation contextuelle". Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00847494.
Texto completoFeydy, Jean. "Analyse de données géométriques, au delà des convolutions". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASN017.
Texto completoGeometric data analysis, beyond convolutionsTo model interactions between points, a simple option is to rely on weighted sums known as convolutions. Over the last decade, this operation has become a building block for deep learning architectures with an impact on many applied fields. We should not forget, however, that the convolution product is far from being the be-all and end-all of computational mathematics.To let researchers explore new directions, we present robust, efficient and principled implementations of three underrated operations: 1. Generic manipulations of distance-like matrices, including kernel matrix-vector products and nearest-neighbor searches.2. Optimal transport, which generalizes sorting to spaces of dimension D > 1.3. Hamiltonian geodesic shooting, which replaces linear interpolation when no relevant algebraic structure can be defined on a metric space of features.Our PyTorch/NumPy routines fully support automatic differentiation and scale up to millions of samples in seconds. They generally outperform baseline GPU implementations with x10 to x1,000 speed-ups and keep linear instead of quadratic memory footprints. These new tools are packaged in the KeOps (kernel methods) and GeomLoss (optimal transport) libraries, with applications that range from machine learning to medical imaging. Documentation is available at: www.kernel-operations.io/keops and /geomloss
Örsvuran, Rıdvan. "Vers des modèles anisotropes et anélastiques de la Terre globale : Observables et la paramétrisation de l'inversion des formes d'ondes complètes". Thesis, Université Côte d'Azur, 2021. http://www.theses.fr/2021COAZ4015.
Texto completoSeismic waves are our primary tools to see the Earth’s interior and draw inferences on its structural, thermal and chemical properties. Seismic tomography, similar to medical tomography, is a powerful technique to obtain 3D computed tomography scan (CT scan) images of the Earth’s interior using seismic waves generated by seismic sources such as earthquakes, ambient noise or controlled explosions. It is crucial to improve the resolution of tomographic images to better understand the internal dynamics of our planet driven by the mantle convection, that directly control surface processes, such as plate tectonics. To this end, at the current resolution of seismic tomography, full physics of (an)elastic wave propagation must be taken into account.The adjoint method is an efficient full-waveform inversion (FWI) technique to take 3D seismic wave propagation into account in tomography to construct high-resolution seismic images. In this thesis, I develop and demonstrate new measurements for global-scale adjoint inversions such as the implementation of double-difference traveltime and waveform misfits. Furthermore, I investigate different parameterizations to better capture Earth’s physics in the inverse problem, such as addressing the azimuthal anisotropy and anelasticity in the Earth’s mantle.My results suggest that double-difference misfits applied to dense seismic networks speed up the convergence of FWI and help increase the resolution underneath station clusters. I further observe that double-difference measurements can also help reduce the bias in data coverage towards the cluster of stations.Earth’s lithosphere and upper mantle show significant evidence of anisotropy as a result of its composition and deformation. Starting from the recent global adjoint tomography model GLAD-M25, which is the successor of GLAD-M15 and transversely isotropic in the upper mantle, my goal is to construct an azimuthally anisotropic global model of the upper mantle. I performed 10 iterations using the multitaper traveltimes combined with double difference measurements made on paired stations of minor- and major-arc surface waves. The results after 10 iterations, in general, show the global anisotropic pattern consistent with plate motions and achieve higher resolution in areas with dense seismic coverage such as in North America and Europe.Attenuation is also another key parameter for determining the partial melt, water content and thermal variations in the mantle. In the last chapter, I investigate anelastic adjoint inversions to ultimately construct a global attenuation mantle model by the simultaneous inversion of elastic and anelastic parameters assimilating both the phase and amplitude information, which will lead to exact FWI at the global scale. I investigate the trade-off between elastic and anelastic parameters based on 2D synthetic tests to define a strategy for 3D global FWIs. I also explore the effect of different measurements for simultaneously and sequentially inverted elastic and anelastic parameters. The 2D test results suggest that the envelope misfit performs best at earlier iterations by reducing the nonlinearity of the FWI. After analyzing the effect of different radially-symmetric attenuation models on seismic waveforms by performing forward simulations in various 1D and 3D elastic/anelastic models, the results suggest the necessity of simultaneous elastic/anelastic inversions to also improve the elastic structure as attenuation cause not only amplitude anomalies but also significant physical dispersion, particularly on surface waves. I performed one global simultaneous iteration of elastic and anelastic parameters using GLAD-M25 and its 1D anelastic model QRF12 as the starting models with a dataset of 253 earthquakes. The preliminary results are promising depicting, for instance, the high and low attenuation in the West and East coasts of North America
Cuingnet, Rémi. "Contributions à l'apprentissage automatique pour l'analyse d'images cérébrales anatomiques". Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00602032.
Texto completoCapítulos de libros sobre el tema "Imagerie computationnelle"
DUCROS, Nicolas. "Une introduction à l’imagerie computationnelle monodétecteur". En Imageries optiques non conventionnelles pour la biologie, 247–74. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9132.ch8.
Texto completoActas de conferencias sobre el tema "Imagerie computationnelle"
Lefort, Claire, Mathieu Chalvidal, Alexis Parenté, Véronique BLANQUET, Henri Massias, Laetitia MAGNOL y Emilie Chouzenoux. "Imagerie 3D par microscopie multiphotonique appliquée aux sciences du vivant : la chaine instrumentale et computationnelle FAMOUS". En Les journées de l'interdisciplinarité 2022. Limoges: Université de Limoges, 2022. http://dx.doi.org/10.25965/lji.221.
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