Il existe un grand nombre de modalités permettant l'acquisition d'un objet de manière non destructrice (Scanner à Rayons X, micro-scanner, Ondes Térahertz, Microscopie Électronique de Transmission, etc). Ces outils acquièrent un ensemble de projections autour de l'objet et une étape de reconstruction aboutit à une représentation de l'espace acquis. La principale limitation de ces méthodes est qu'elles s'appuient sur une modélisation continue de l'espace alors qu'elles sont exploitées dans un domaine fini. L'étape de discrétisation qui en résulte est une source d'erreurs sur les images produites. De plus, la phase d'acquisition ne s'effectue pas de manière idéale et peut donc être entachée d'artéfacts et de bruits. Un grand nombre de méthodes, directes ou itératives, ont été développées pour tenter de réduire les erreurs et reproduire une image la plus représentative possible de la réalité. Un panorama de ces reconstructions est proposé ici et est coloré par une étude de la qualité, de la précision et de la résistances aux bruits d'acquisition.Puisque la discrétisation constitue l'une des principales limitations, nous cherchons ensuite à adapter des méthodes discrètes pour la reconstruction de données réelles. Ces méthodes sont exactes dans un domaine fini mais ne sont pas adaptées à une acquisition réelle, notamment à cause de leur sensibilité aux erreurs. Nous proposons donc un lien entre les deux mondes et développons de nouvelles méthodes discrètes plus robustes aux bruits. Enfin, nous nous intéressons au problème des données manquantes, i.e. lorsque l'acquisition n'est pas uniforme autour de l'objet, à l'origine de déformations dans les images reconstruites. Comme les méthodes discrètes sont insensibles à cet effet nous proposons une amorce de solution utilisant les outils développés dans nos travaux. / A large kind of methods are available now to acquire an object in a non-destructive way (X-Ray scanner, micro-scanner, Tera-hertz waves, Transmission Electron Microscopy, etc). These tools acquire a projection set around the object and a reconstruction step leads to a representation of the acquired domain. The main limitation of these methods is that they rely on a continuous domain modeling wheareas they compute in a finite domain. The resulting discretization step sparks off errors in obtained images. Moreover, the acquisition step is not performed ideally and may be corrupted by artifacts and noises. Many direct or iterative methods have been developped to try to reduce errors and to give a better representative image of reality. An overview of these reconstructions is proposed and it is enriched with a study on quality, precision and noise robustness.\\Since the discretization is one of the major limitations, we try to adjust discrete methods for the reconstruction of real data. These methods are accurate in a finite domain but are not suitable for real acquisition, especially because of their error sensitivity. Therefore, we propose a link between the two worlds and we develop new discrete and noise robust methods. Finally, we are interesting in the missing data problem, i.e. when the acquisition is not uniform around the object, giving deformations into reconstructed images. Since discrete reconstructions are insensitive to this effect, we propose a primer solution using the tools developed previously.

Os métodos incrementais pertencem a uma classe de métodos iterativos que divide o conjunto de dados em subconjuntos ordenados, e que atualiza a imagem ao processar cada subconjunto (sub-iterações). Isso acelera a convergência das reconstruções, e imagens de qualidade são obtidas em menos iterações. No entanto, a cada sub-iteração é necessário calcular os operadores de projeção e retroprojeção, resultando no custo computacional de ordem O(n3) para a reconstrução de imagens de dimensão × . Por outro lado, algumas alternativas baseadas na interpolação em uma grade regular no espaço de Fourier ou em transformadas rápidas não-uniformes, dentre outras ideias, foram desenvolvidas a fim de aliviar esse custo computacional. Além disso, diversas abordagens foram bem sucedidas em acelerar o cálculo das iterações de algoritmos clássicos, mas nenhuma havia sido utilizada em conjunto com os métodos incrementais. Neste trabalho é proposta uma nova abordagem em que a técnica de transformada rápida de Fourier não uniforme (NFFT) é utilizada nas sub-iterações de métodos incrementais com o objetivo de efetuar de forma eficiente os cálculos numericamente mais intensos: a projeção e a retroprojeção, resultando em métodos incrementais com complexidade O(n2 log n ). Os métodos propostos são aplicados à tomografia por radiação síncrotron e os resultados da pesquisa mostram um bom desempenho. / Incremental methods belong to a class of iterative methods that divide the data set into ordered subsets, and which update the image when processing each subset (sub-iterations). It accelerates the reconstruction convergence and quality images are obtained in fewer iterations. However, it is necessary to compute the projection and backprojection operators in each sub-iteration, resulting in the computational cost of O(n3) flops for × images. On the other hand, some alternatives based on interpolation over a regular grid on the Fourier space or on nonequispaced fast transforms, among other ideas, were developed in order to alleviate the computational cost. In addition, several approaches substantially speed up the computation of the iterations of classical algorithms, but the incremental methods had not been benefited from these techniques. In this work, a new approach is proposed in which the nonequispaced fast Fourier transform (NFTT) is used in each subiteration of incremental methods in order to perform the numerically intensive calculations efficiently: the projection and backprojection, resulting in incremental methods with complexity O(n2 log n ). The proposed methods are applied to the synchrotron radiation tomography and the results show a good performance.

Morphology change analysis of aorta images acquired from biological experiments plays a critical role in exploring the relationship between lamina thickness (LT), interlamellar distance (ILD) and fragmentation (furcation points) with respect to pathological conditions. An automated software tool now is available to extract elastic laminae (EL) and measure LT, ILD and fragmentation along their ridge lines in a fine detailed aspect. A statistical randomized complete block design (RCBD) and F-test were used to assess potential (non)-uniformity of LT and ILD along both radial and circumferential directions. Illustrative results for both normotensive and hypertensive thoracic porcine aorta revealed marked heterogeneity along the radial direction in nearly stress-free samples. Quantifying furcation point densities were also found that can offer new information about potential elastin fragmentation, particularly in response to increased loading due to hypertension. Furthermore, when biological scientists analyze the elastic lamina structure, how to automatically generate a macro-level geometric parameter mapping might greatly help them understand the over-all morphology changes of blood vessel cross section. In this dissertation, another automated system is designed to quickly locate more pronounced EL branches to construct layer level abstraction of LT/ILD measurements and transform the sparse pixel level information to dense normalized Virtual Layer Matrix (VLM). The system can automatically compute the EL orientations, identify pronounced ELs, transform the denoised LT measurement points onto a VLM and then provide statistics/segmentation analysis. By applying the k-means segmentation technique to VLMs of LT-ILD, one can easily delineate regions of normal vs. hypertrophic and/or hyperplasia LT-ILD measurements for cross-image references.

Since the advent of the first ionizing radiation imaging devices initiated by Godfrey Newbold Hounsfield and Allan MacLeod Cormack, Nobel Prizes in 1979, the requirement for new non-invasive imaging techniques has grown. These techniques rely upon the properties of penetration in the matter of X and gamma radiation for detecting a hidden structure without destroying the illuminated environment. They are used in many fields ranging from medical imaging to non-destructive testing through. However, the techniques used so far suffer severe degradation in the quality of measurement and reconstructed images. Usually approximated by a noise, these degradations require to be compensated or corrected by collimating devices and often expensive filtering. These degradation is mainly due to scattering phenomena which may constitute up to 80% of the emitted radiation in biological tissue. In the 80's a new concept has emerged to circumvent this difficulty : the Compton scattering tomography (CST).This new approach proposes to measure the scattered radiation considering energy ranges ( 140-511 keV) where the Compton effect is the phenomenon of leading broadcast. The use of such imaging devices requires a deep understanding of the interactions between radiation and matter to propose a modeling, consistent with the measured data, which is essential to image reconstruction. In conventional imaging systems (which measure the primary radiation) the Radon transformdefined on the straight lines emerged as the natural modeling. But in Compton scattering tomography, the measured information is related to the scattering energy and thus the scattering angle. Thus the circular geometry induced by scattering phenomenon makes the classical Radon transform inadequate.In this context, it becomes necessary to provide such Radon transforms on broader geometric manifolds.The study of the Radon transform on new manifolds of curves becomes necessary to provide theoretical needs for new imaging techniques. Cormack, himself, was the first to extend the properties of the conventional Radon transform of a family of curves of the plane. Thereafter several studies have been done in order to study the Radon transform defined on different varieties of circles, spheres, broken lines ... . In 1994 S.J. Norton proposed the first modality in Compton scattering tomography modeled by a Radon transform on circular arcs, the CART1 here. In 2010, Nguyen and Truong established the inversion formula of a Radon transform on circular arcs, CART2, to model the image formation in a new modality in Compton scattering tomography. The geometry involved in the integration support of new modalities in Compton scattering tomography lead them to demonstrate the invertibility of the Radon transform defined on a family of Cormack-type curves, called C_alpha. They illustrated the inversion procedure in the case of a new transform, the CART3, modeling a new modeling of Compton scattering tomography. Based on the work of Cormack and Truong and Nguyen, we propose to establish several properties of the Radon transform on the family C_alpha especially on C1. We have thus demonstrated two inversion formulae that reconstruct the original image via its circular harmonic decomposition and itscorresponding transform. These formulae are similar to those established by Truong and Nguyen. We finally established the well-known filtered back projection and singular value decomposition in the case alpha = 1. All results established in this study provide practical problems of image reconstruction associated with these new transforms. In particular we were able to establish new inversion methods for transforms CART1,2,3 as well as numerical approaches necessary for the implementation of these transforms. All these results enable to solve problems of image formation and reconstruction related to three Compton scattering tomography modalities.In addition we propose to improve models and algorithms es

Os métodos incrementais pertencem a uma classe de métodos iterativos que divide o conjunto de dados em subconjuntos ordenados, e que atualiza a imagem ao processar cada subconjunto (sub-iterações). Isso acelera a convergência das reconstruções, e imagens de qualidade são obtidas em menos iterações. No entanto, a cada sub-iteração é necessário calcular os operadores de projeção e retroprojeção, resultando no custo computacional de ordem O(n3) para a reconstrução de imagens de dimensão × . Por outro lado, algumas alternativas baseadas na interpolação em uma grade regular no espaço de Fourier ou em transformadas rápidas não-uniformes, dentre outras ideias, foram desenvolvidas a fim de aliviar esse custo computacional. Além disso, diversas abordagens foram bem sucedidas em acelerar o cálculo das iterações de algoritmos clássicos, mas nenhuma havia sido utilizada em conjunto com os métodos incrementais. Neste trabalho é proposta uma nova abordagem em que a técnica de transformada rápida de Fourier não uniforme (NFFT) é utilizada nas sub-iterações de métodos incrementais com o objetivo de efetuar de forma eficiente os cálculos numericamente mais intensos: a projeção e a retroprojeção, resultando em métodos incrementais com complexidade O(n2 log n ). Os métodos propostos são aplicados à tomografia por radiação síncrotron e os resultados da pesquisa mostram um bom desempenho. / Incremental methods belong to a class of iterative methods that divide the data set into ordered subsets, and which update the image when processing each subset (sub-iterations). It accelerates the reconstruction convergence and quality images are obtained in fewer iterations. However, it is necessary to compute the projection and backprojection operators in each sub-iteration, resulting in the computational cost of O(n3) flops for × images. On the other hand, some alternatives based on interpolation over a regular grid on the Fourier space or on nonequispaced fast transforms, among other ideas, were developed in order to alleviate the computational cost. In addition, several approaches substantially speed up the computation of the iterations of classical algorithms, but the incremental methods had not been benefited from these techniques. In this work, a new approach is proposed in which the nonequispaced fast Fourier transform (NFTT) is used in each subiteration of incremental methods in order to perform the numerically intensive calculations efficiently: the projection and backprojection, resulting in incremental methods with complexity O(n2 log n ). The proposed methods are applied to the synchrotron radiation tomography and the results show a good performance.

In this thesis an algorithm is demonstrated for the reconstruction of hard-field Tomography images through localized block areas, obtained in parallel and from a multiresolution framework. Block areas are subsequently tiled to put together the full size image. Given its properties to preserve its compact support after being ramp filtered, the wavelet transform has received to date much attention as a promising solution in radiation dose reduction in medical imaging, through the reconstruction of essentially localised regions. In this work, this characteristic is exploited with the aim of reducing the time and complexity of the standard reconstruction algorithm. Independently reconstructing block images with geometry allowing to cover completely the reconstructed frame as a single output image, allows the individual blocks to be reconstructed in parallel, and to experience its performance in a multiprocessor hardware reconfigurable system (i.e. FPGA). Projection data from simulated Radon Transform (RT) was obtained at 180 evenly spaced angles. In order to define every relevant block area within the sinogram, forward RT was performed over template phantoms representing block frames. Reconstruction was then performed in a domain beyond the block frame limits, to allow calibration overlaps when fitting of adjacent block images. The 256 by 256 Shepp-Logan phantom was used to test the methodology of both parallel multiresolution and parallel block reconstruction generalisations. It is shown that the reconstruction time of a single block image in a 3-scale multiresolution framework, compared to the standard methodology, performs around 48 times faster. By assuming a parallel implementation, it can implied that the reconstruction time of a single tile, should be very close related to the reconstruction time of the full size and resolution image.

La résonance paramagnétique électronique est une technologie permettant de localiser et de caractériser les radicaux libres, fondée sur la propriété de résonance des électrons libres lorsqu’ils sont placés dans un champ magnétique spécifique. Afin d’augmenter la qualité des reconstructions obtenues par des dispositifs d’imagerie de résonance paramagnétique électronique, ce travail propose l’utilisation de méthodes variationnelles pour inverser le modèle de formation des images, qui combine une convolution avec une transformée de Radon. La fonctionnelle proposée repose sur la norme L2 pour le terme d’attache aux données, et sur la variation totale et une seminorme de Besov pour le terme de régularisation. La seminorme de Besov est implémentée grâce à la transformée en curvelets et à la norme L1 qui permet d’appliquer un critère de parcimonie. Les propriétés de ces termes de régularisation permettent de reconstruire des images à la fois pertinentes dans les zones où l’acquisition des données est insuffisante, notamment sur les bords, et suffisamment détaillées dans les zones où l’échantillon est texturé. L’augmentation de la qualité des images reconstruites permet d’envisager des acquisitions sur des durées réduites, ouvrant la voie à des expériences in vivo ou cliniques actuellement limitées par des durées d’acquisition de l’ordre de plusieurs dizaines de minutes. Les algorithmes de minimisation primal-dual de Chambolle-Pock et FISTA sont utilisés pour résoudre les problèmes d’optimisation que pose l’utilisation de méthodes variationnelles. L’étude détaillée du modèle direct permet de mettre en évidence une structure de Toeplitz, dont les propriétés sont utilisées pour résoudre le problème inverse en évitant le recours à la rétroprojection filtrée ou aux transformées de Fourier non-uniformes. Des simulations numériques sont menées sur le fantôme de Shepp-Logan, et valident le modèle proposé qui surpasse à la fois visuellement et quantitativement les techniques de reconstruction couramment utilisées, combinant déconvolution et rétroprojection filtrée. Des reconstructions menées sur des acquisitions réelles, consistant en un échantillon papier d’une encre paramagnétique et en une phalange distale irradiée, valident par l’expérience le choix des fonctionnelles utilisées pour inverser le modèle direct. La grande souplesse de la méthode variationnelle proposée permet d’adapter la fonctionnelle au problème de la séparation de sources qui se pose lorsque deux molécules paramagnétiques différentes sont présentes au sein d’un même échantillon. La fonctionnelle proposée permet de séparer les deux molécules dans le cadre d’une acquisition classique d’imagerie de résonance paramagnétique électronique, ce qui n’était possible jusqu’alors que sur des acquisitions dites hyperspectrales beaucoup plus gourmandes en temps. / Spatial electron paramagnetic resonance imaging (EPRI) is a recent method to localize and characterize free radicals in vivo or in vitro, leading to applications in material and biomedical sciences. To improve the quality of the reconstruction obtained by EPRI, a variational method is proposed to inverse the image formation model. It is based on a least-square data-fidelity term and the total variation and Besov seminorm for the regularization term. To fully comprehend the Besov seminorm, an implementation using the curvelet transform and the L1 norm enforcing the sparsity is proposed. It allows our model to reconstruct both image where acquisition information are missing and image with details in textured areas, thus opening possibilities to reduce acquisition times. To implement the minimization problem using the algorithm developed by Chambolle and Pock, a thorough analysis of the direct model is undertaken and the latter is inverted while avoiding the use of filtered backprojection (FBP) and of non-uniform Fourier transform. Numerical experiments are carried out on simulated data, where the proposed model outperforms both visually and quantitatively the classical model using deconvolution and FBP. Improved reconstructions on real data, acquired on an irradiated distal phalanx, were successfully obtained. Due to its great versatility, the variational approach is easily extended to the source separation problem which happens when two different paramagnetic species are present in the sample. The objective function is consequently modified, and a classic EPRI acquisition yields two images, one for each species. Until now, source separation could only be applied to hyperspectral EPRI data, much more costly in acquisition time.

The traditional Computed Tomography (CT) is based on the Radon Transform and its inversion. The Radon transform uses parallel beam geometry and its inversion is based on the Fourier slice theorem. In practice, it is very efficient to employ a back-projection algorithm in connection with the Fast Fourier Transform, and which can be interpreted as a 1-D filtering across the radial dimension of the 2-D Fourier plane of the transformed image. This approach can easily be adapted to windowing techniques in the frequency domain, giving the capability to reduce image noise. In this work we are investigating the capabilities of the so called Kaiser window (giving an optimal trade-off between the main lobe energy and the sidelobe suppression) to achieve a near optimal trade-off between the noise reduction and the image sharpness in the context of Radon inversion. Finally, we simulate our image reconstruction using MATLAB software and compare and estimate our results based on the normalized Least Square Error (LSE). We conclude that the Kaiser window can be used to achieve an optimal trade-off between noise reduction and sharpness in the image, and hence outperforms all the other classical window function in this regard.

yes / In the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil’s parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out.

The present thesis considers the problem of reconstructing a function f that is defined on the d-dimensional unit sphere from its mean values along hyperplane sections. In case of the two-dimensional sphere, these plane sections are circles. In many tomographic applications, however, only limited data is available. Therefore, one is interested in the reconstruction of the function f from its mean values with respect to only some subfamily of all hyperplane sections of the sphere. Compared with the full data case, the limited data problem is more challenging and raises several questions. The first one is the injectivity, i.e., can any function be uniquely reconstructed from the available data? Further issues are the stability of the reconstruction, which is closely connected with a description of the range, as well as the demand for actual inversion methods or algorithms. We provide a detailed coverage and answers of these questions for different families of hyperplane sections of the sphere such as vertical slices, sections with hyperplanes through a common point and also incomplete great circles. Such reconstruction problems arise in various practical applications like Compton camera imaging, magnetic resonance imaging, photoacoustic tomography, Radar imaging or seismic imaging. Furthermore, we apply our findings about spherical means to the cone-beam transform and prove its singular value decomposition. / Die vorliegende Arbeit beschäftigt sich mit dem Problem der Rekonstruktion einer Funktion f, die auf der d-dimensionalen Einheitssphäre definiert ist, anhand ihrer Mittelwerte entlang von Schnitten mit Hyperebenen. Im Fall d=2 sind diese Schnitte genau die Kreise auf der Sphäre. In vielen tomografischen Anwendungen sind aber nur eingeschränkte Daten verfügbar. Deshalb besteht das Interesse an der Rekonstruktion der Funktion f nur anhand der Mittelwerte bestimmter Familien von Hyperebenen-Schnitten der Sphäre. Verglichen mit dem Fall vollständiger Daten birgt dieses Problem mehrere Herausforderungen und Fragen. Die erste ist die Injektivität, also können alle Funktionen anhand der gegebenen Daten eindeutig rekonstruiert werden? Weitere Punkte sind die die Frage nach der Stabilität der Rekonstruktion, welche eng mit einer Beschreibung der Bildmenge verbunden ist, sowie der praktische Bedarf an Rekonstruktionsmethoden und -algorithmen. Diese Arbeit gibt einen detaillierten Überblick und Antworten auf diese Fragen für verschiedene Familien von Hyperebenen-Schnitten, angefangen von vertikalen Schnitten über Schnitte mit Hyperebenen durch einen festen Punkt sowie Kreisbögen. Solche Rekonstruktionsprobleme treten in diversen Anwendungen auf wie der Bildgebung mittels Compton-Kamera, Magnetresonanztomografie, fotoakustischen Tomografie, Radar-Bildgebung sowie der Tomografie seismischer Wellen. Weiterhin nutzen wir unsere Ergebnisse über sphärische Mittelwerte, um eine Singulärwertzerlegung für die Kegelstrahltomografie zu zeigen.