Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

International audienceStochastic watershed is an image segmentation technique based on mathematical morphology which produces a probability density function of image contours. Estimated probabilities depend mainly on local distances between pixels. This paper introduces a variant of stochastic watershed where the probabilities of contours are computed from a Gaussian model of image regions. In this framework, the basic ingredient is the distance between pairs of regions, hence a distance between normal distributions. Hence several alternatives of statistical distances for normal distributions are compared, namely Bhattacharyya distance, Hellinger metric distance and Wasserstein metric distance

Advanced techniques in medical image segmentation of the liver

Tesis por compendio[EN] Image segmentation is, along with multimodal and monomodal registration, the operation with the greatest applicability in medical image processing. There are many operations and filters, as much as applications and cases, where the segmentation of an organic tissue is the first step. The case of liver segmentation in radiological images is, after the brain, that on which the highest number of scientific publications can be found. This is due, on the one hand, to the need to continue innovating in existing algorithms and, on the other hand, to the applicability in many situations related to diagnosis, treatment and monitoring of liver diseases but also for clinical planning. In the case of magnetic resonance imaging (MRI), only in recent years some solutions have achieved good results in terms of accuracy and robustness in the segmentation of the liver. However these algorithms are generally not user-friendly. In the case of computed tomography (CT) scans more methodologies and solutions have been developed but it is difficult to find a good trade-off between accuracy and practical clinical use. To improve the state-of-the-art in both cases (MRI and CT), a common methodology to design and develop two liver segmentation algorithms in those imaging modalities has been proposed in this thesis. The second step has been the validation of both algorithms. In the case of CT images, there exist public databases with images segmented manually by experts that the scientific community uses as a common link for the validation and comparison of their algorithms. The validation is done by obtaining certain coefficients of similarity between the manual and the automatic segmentation. This way of validating the accuracy of the algorithm has been followed in this thesis, except in the case of magnetic resonance imaging because, at present, there are no databases publicly available. In this case, there aren't public or accessible images. Accordingly, a private database has been created where several expert radiologists have manually segmented different studies of patients that have been used as a reference. This database is composed by 17 studies (with more than 1,500 images), so the validation of this method in MRI is one of the more extensive currently published. In the validation stage, an accuracy above 90% in the Jaccard and Dice coefficients has been achieved. The vast majority of the compared authors achieves similar values. However, in general, the algorithms proposed in this thesis are more user-friendly for clinical environments because the computational cost is lower, the clinical interaction is non-existent and it is not necessary an initiation in the case of the magnetic resonance algorithm and a small initiation (it is only necessary to introduce a manual seed) for the computed tomography algorithm. In this thesis, a third hypothesis that makes use of the results of liver segmentation in MRI combined to augmented reality algorithms has also been developed. Specifically, a real and innocuous study, non-invasive for clinician and patient has been designed and validated through it has been shown that the use of this technology creates benefits in terms of greater accuracy and less variability versus the non-use in a particular case of laparoscopic surgery.[ES] La segmentación de imágenes es, junto al registro multimodal y monomodal, la operación con mayor aplicabilidad en tratamiento digital de imagen médica. Son multitud las operaciones y filtros, así como las aplicaciones y casuística, que derivan de una segmentación de un tejido orgánico. El caso de segmentación del hígado en imágenes radiológicas es, después del cerebro, la que mayor número de publicaciones científicas podemos encontrar. Esto es debido por un lado a la necesidad de seguir innovando en los algoritmos ya existentes y por otro a la gran aplicabilidad que tiene en muchas situaciones relacionadas con el diagnóstico, tratamiento y seguimiento de patologías hepáticas pero también para la planificación clínica de las mismas. En el caso de imágenes de resonancia magnética, sólo en los últimos años han aparecido soluciones que consiguen buenos resultados en cuanto a precisión y robustez en la segmentación del hígado. Sin embargo dichos algoritmos, por lo general son poco utilizables en el ambiente clínico. En el caso de imágenes de tomografía computarizada encontramos mucha más variedad de metodologías y soluciones propuestas pero es difícil encontrar un equilibrio entre precisión y uso práctico clínico. Es por ello que para mejorar el estado del arte en ambos casos (imágenes de resonancia magnética y tomografía computarizada) en esta tesis se ha planteado una metodología común a la hora de diseñar y desarrollar sendos algoritmos de segmentación del hígado en las citadas modalidades de imágenes anatómicas. El segundo paso ha sido la validación de ambos algoritmos. En el caso de imágenes de tomografía computarizada existen bases de datos públicas con imágenes segmentadas manualmente por expertos y que la comunidad científica suele utilizar como nexo común a la hora de validar y posteriormente comparar sus algoritmos. La validación se hace mediante la obtención de determinados coeficientes de similitud entre la imagen segmentada manualmente por los expertos y las que nos proporciona el algoritmo. Esta forma de validar la precisión del algoritmo ha sido la seguida en esta tesis, con la salvedad que en el caso de imágenes de resonancia magnética no existen bases de datos de acceso público. Por ello, y para este caso, lo que se ha hecho es la creación previa de una base de datos propia donde diferentes expertos radiólogos han segmentado manualmente diferentes estudios de pacientes con el fin de que puedan servir como referencia y se pueda seguir la misma metodología que en el caso anterior. Dicha base de datos ha hecho posible que la validación se haga en 17 estudios (con más de 1.500 imágenes), lo que convierte la validación de este método de segmentación del hígado en imágenes de resonancia magnética en una de las más extensas publicadas hasta la fecha. La validación y posterior comparación han dejado patente una precisión superior al 90% reflejado en el coeficiente de Jaccard y Dice, muy en consonancia con valores publicados por la inmensa mayoría de autores que se han podido comparar. Sin embargo, y en general, los algoritmos planteados en esta tesis han obtenido unos criterios de uso mucho mayores, ya que en general presentan menores costes de computación, una interacción clínica casi nula y una iniciación nula en el caso del algoritmo de resonancia magnética y casi nula en el caso de algoritmos de tomografía computarizada. En esta tesis, también se ha abordado un tercer punto que hace uso de los resultados obtenidos en la segmentación del hígado en imágenes de resonancia magnética. Para ello, y haciendo uso de algoritmos de realidad aumentada, se ha diseñado y validado un estudio real inocuo y no invasivo para el clínico y para el paciente donde se ha demostrado que la utilización de esta tecnología reporta mayores beneficios en cuanto a mayor precisión y menor variabilidad frente a su no uso en un caso concreto de ciru[CA] La segmentació d'imatges és, al costat del registre multimodal i monomodal, l'operació amb major aplicabilitat en tractament digital d'imatge mèdica. Són multitud les operacions i filtres, així com les aplicacions i casuística, que comencen en la segmentació d'un teixit orgànic. El cas de segmentació del fetge en imatges radiològiques és, després del cervell, la que major nombre de publicacions científiques podem trobar. Això és degut per una banda a la necessitat de seguir innovant en els algoritmes ja existents i per un altre a la gran aplicabilitat que té en moltes situacions relacionades amb el diagnòstic, tractament i seguiment de patologies hepàtiques però també per a la planificació clínica de les mateixes. En el cas d'imatges de ressonància magnètica, només en els últims anys han aparegut solucions que aconsegueixen bons resultats quant a precisió i robustesa en la segmentació del fetge. No obstant això aquests algoritmes, en general són poc utilitzables en l'ambient clínic. En el cas d'imatges de tomografia computeritzada trobem molta més varietat de metodologies i solucions proposades però és difícil trobar un equilibri entre precisió i ús pràctic clínic. És per això que per millorar l'estat de l'art en els dos casos (imatges de ressonància magnètica i tomografia computeritzada) en aquesta tesi s'ha plantejat una metodologia comuna a l'hora de dissenyar i desenvolupar dos algoritmes de segmentació del fetge en les esmentades modalitats d'imatges anatòmiques. El segon pas ha estat la validació de tots dos algoritmes. En el cas d'imatges de tomografia computeritzada hi ha bases de dades públiques amb imatges segmentades manualment per experts i que la comunitat científica sol utilitzar com a nexe comú a l'hora de validar i posteriorment comparar els seus algoritmes. La validació es fa mitjançant l'obtenció de determinats coeficients de similitud entre la imatge segmentada manualment pels experts i les que ens proporciona l'algoritme. Aquesta forma de validar la precisió de l'algoritme ha estat la seguida en aquesta tesi, amb l'excepció que en el cas d'imatges de ressonància magnètica no hi ha bases de dades d'accés públic. Per això, i per a aquest cas, el que s'ha fet és la creació prèvia d'una base de dades pròpia on diferents experts radiòlegs han segmentat manualment diferents estudis de pacients amb la finalitat que puguen servir com a referència i es puga seguir la mateixa metodologia que en el cas anterior. Aquesta base de dades ha fet possible que la validació es faja en 17 estudis (amb més de 1.500 imatges), cosa que converteix la validació d'aquest mètode de segmentació del fetge en imatges de ressonància magnètica en una de les més extenses publicades fins a la data. La validació i posterior comparació han deixat patent una precisió superior al 90 \% reflectit en el coeficient de \ textit {Jaccard} i \ textit {Dice}, molt d'acord amb valors publicats per la immensa majoria d'autors en que s'ha pogut comparar. No obstant això, i en general, els algoritmes plantejats en aquesta tesi han obtingut uns criteris d'ús molt més grans, ja que en general presenten menors costos de computació, una interacció clínica quasi nul·la i una iniciació nul·la en el cas de l'algoritme de ressonància magnètica i quasi nul·la en el cas d'algoritmes de tomografia computeritzada. En aquesta tesi, també s'ha abordat un tercer punt que fa ús dels resultats obtinguts en la segmentació del fetge en imatges de ressonància magnètica. Per a això, i fent ús d'algoritmes de realitat augmentada, s'ha dissenyat i validat un estudi real innocu i no invasiu per al clínic i per al pacient on s'ha demostrat que la utilització d'aquesta tecnologia reporta més beneficis pel que fa a major precisió i menor variabilitat enfront del seu no ús en un cas concret de cirurgia amb laparoscòpia.López Mir, F. (2015). Advanced techniques in medical image segmentation of the liver [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59428TESISPremios Extraordinarios de tesis doctoralesCompendi

Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods

Facial soft tissue segmentation

The importance of the face for socio-ecological interaction is the cause for a high demand on any surgical intervention on the facial musculo-skeletal system. Bones and soft-tissues are of major importance for any facial surgical treatment to guarantee an optimal, functional and aesthetical result. For this reason, surgeons want to pre-operatively plan, simulate and predict the outcome of the surgery allowing for shorter operation times and improved quality. Accurate simulation requires exact segmentation knowledge of the facial tissues. Thus semi-automatic segmentation techniques are required. This thesis proposes semi-automatic methods for segmentation of the facial soft-tissues, such as muscles, skin and fat, from CT and MRI datasets, using a Markov Random Fields (MRF) framework. Due to image noise, artifacts, weak edges and multiple objects of similar appearance in close proximity, it is difficult to segment the object of interest by using image information alone. Segmentations would leak at weak edges into neighboring structures that have a similar intensity profile. To overcome this problem, additional shape knowledge is incorporated in the energy function which can then be minimized using Graph-Cuts (GC). Incremental approaches by incorporating additional prior shape knowledge are presented. The proposed approaches are not object specific and can be applied to segment any class of objects be that anatomical or non-anatomical from medical or non-medical image datasets, whenever a statistical model is present. In the first approach a 3D mean shape template is used as shape prior, which is integrated into the MRF based energy function. Here, the shape knowledge is encoded into the data and the smoothness terms of the energy function that constrains the segmented parts to a reasonable shape. In the second approach, to improve handling of shape variations naturally found in the population, the fixed shape template is replaced by a more robust 3D statistical shape model based on Probabilistic Principal Component Analysis (PPCA). The advantages of using the Probabilistic PCA are that it allows reconstructing the optimal shape and computing the remaining variance of the statistical model from partial information. By using an iterative method, the statistical shape model is then refined using image based cues to get a better fitting of the statistical model to the patient's muscle anatomy. These image cues are based on the segmented muscle, edge information and intensity likelihood of the muscle. Here, a linear shape update mechanism is used to fit the statistical model to the image based cues. In the third approach, the shape refinement step is further improved by using a non-linear shape update mechanism where vertices of the 3D mesh of the statistical model incur the non-linear penalty depending on the remaining variability of the vertex. The non-linear shape update mechanism provides a more accurate shape update and helps in a finer shape fitting of the statistical model to the image based cues in areas where the shape variability is high. Finally, a unified approach is presented to segment the relevant facial muscles and the remaining facial soft-tissues (skin and fat). One soft-tissue layer is removed at a time such as the head and non-head regions followed by the skin. In the next step, bones are removed from the dataset, followed by the separation of the brain and non-brain regions as well as the removal of air cavities. Afterwards, facial fat is segmented using the standard Graph-Cuts approach. After separating the important anatomical structures, finally, a 3D fixed shape template mesh of the facial muscles is used to segment the relevant facial muscles. The proposed methods are tested on the challenging example of segmenting the masseter muscle. The datasets were noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. dental fillings and dental implants. Qualitative and quantitative experimental results show that by incorporating prior shape knowledge leaking can be effectively constrained to obtain better segmentation results

Advanced Visual Computing for Image Saliency Detection

Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection methods can generally be categorized as bottom-up methods and top-down methods, and the prevalent deep neural network (DNN) has begun to show its applications in saliency detection in recent years. However, the challenges in existing methods, such as problematic pre-assumption, inefficient feature integration and absence of high-level feature learning, prevent them from superior performances. In this thesis, to address the limitations above, we have proposed multiple novel models with favorable performances. Specifically, we first systematically reviewed the developments of saliency detection and its related works, and then proposed four new methods, with two based on low-level image features, and two based on DNNs. The regularized random walks ranking method (RR) and its reversion-correction-improved version (RCRR) are based on conventional low-level image features, which exhibit higher accuracy and robustness in extracting the image boundary based foreground / background queries; while the background search and foreground estimation (BSFE) and dense and sparse labeling (DSL) methods are based on DNNs, which have shown their dominant advantages in high-level image feature extraction, as well as the combined strength of multi-dimensional features. Each of the proposed methods is evaluated by extensive experiments, and all of them behave favorably against the state-of-the-art, especially the DSL method, which achieves remarkably higher performance against sixteen state-of-the-art methods (including ten conventional methods and six learning based methods) on six well-recognized public datasets. The successes of our proposed methods reveal more potential and meaningful applications of saliency detection in real-life computer vision tasks

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Landscape structure, regimes, and the co-evolution of hydrologic systems

In this dissertation I discuss approaches to building hydrologic understanding in a changing world that go beyond the construction of models of ever-greater complexity. These approaches aim to develop insight into the relationship between catchment properties and hydrologic dynamics using reduced-complexity models, and by looking for patterns that reveal emergent relationships between hydrologic systems and the landscapes they are embedded in. The dissertation proposes a framework for thinking about hydrologic systems in a changing world based on seeking a synthesis between the search for mechanistic descriptions of landscape processes, and the search for explanations for emergent landscape patterns. The dissertation is divided into two parts. The first describes a series of studies considering controls on the propagation of hydrologic variability through the landscape. One discusses the propagation of water and solutes through the vadose zone, another the lateral movement of water through a hillslope, and the third the accumulated effect of many hillslopes on the recession of flows at a watershed outlet. Each case builds on parsimonious representations of hydrologic processes to distill analytical results in terms of landscape and climate properties. These analytical results are used to define `regimes' of hydrologic behavior in which particular properties play decisive roles in the hydrologic system. The studies demonstrate that the regime framework yields insight into controls on the aggregate behavior of hydrologic system that can be used to develop `closure relations' capable of representing the effects of unresolved landscape structure on hydrologic fluxes without resolving them explicitly. The second part of the dissertation is concerned with how the landscape structure controlling the hydrologic dynamics has come to be the way it is, and the role that hydrologic variability plays in its evolution. This question is pursued at a range of scales, using modeling and data analysis. Inter-annual water balance variability across the climates and geologies of the continental US are examined for patterns in the dynamics of co-evolved landscapes. A simple model is then used to illustrate how catchment water-balance is affected by feedbacks between the lateral redistribution of water and the spatial organization of vegetation along the network. This work illustrates how insights into why and how landscape hydrology varies from place to place and through time can be built through a focus on the behavior that emerges from small-scale dynamics, conditioned by the over-arching climate, geology and the contingencies of history. These insights point the way to a new paradigm for hydrology that treats hydrologic systems as integrated wholes that have evolved through time, and will continue to change in the future

Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present

Autonomous vision-based terrain-relative navigation for planetary exploration

Abstract: The interest of major space agencies in the world for vision sensors in their mission designs has been increasing over the years. Indeed, cameras offer an efficient solution to address the ever-increasing requirements in performance. In addition, these sensors are multipurpose, lightweight, proven and a low-cost technology. Several researchers in vision sensing for space application currently focuse on the navigation system for autonomous pin-point planetary landing and for sample and return missions to small bodies. In fact, without a Global Positioning System (GPS) or radio beacon around celestial bodies, high-accuracy navigation around them is a complex task. Most of the navigation systems are based only on accurate initialization of the states and on the integration of the acceleration and the angular rate measurements from an Inertial Measurement Unit (IMU). This strategy can track very accurately sudden motions of short duration, but their estimate diverges in time and leads normally to high landing error. In order to improve navigation accuracy, many authors have proposed to fuse those IMU measurements with vision measurements using state estimators, such as Kalman filters. The first proposed vision-based navigation approach relies on feature tracking between sequences of images taken in real time during orbiting and/or landing operations. In that case, image features are image pixels that have a high probability of being recognized between images taken from different camera locations. By detecting and tracking these features through a sequence of images, the relative motion of the spacecraft can be determined. This technique, referred to as Terrain-Relative Relative Navigation (TRRN), relies on relatively simple, robust and well-developed image processing techniques. It allows the determination of the relative motion (velocity) of the spacecraft. Despite the fact that this technology has been demonstrated with space qualified hardware, its gain in accuracy remains limited since the spacecraft absolute position is not observable from the vision measurements. The vision-based navigation techniques currently studied consist in identifying features and in mapping them into an on-board cartographic database indexed by an absolute coordinate system, thereby providing absolute position determination. This technique, referred to as Terrain-Relative Absolute Navigation (TRAN), relies on very complex Image Processing Software (IPS) having an obvious lack of robustness. In fact, these software depend often on the spacecraft attitude and position, they are sensitive to illumination conditions (the elevation and azimuth of the Sun when the geo-referenced database is built must be similar to the ones present during mission), they are greatly influenced by the image noise and finally they hardly manage multiple varieties of terrain seen during the same mission (the spacecraft can fly over plain zone as well as mountainous regions, the images may contain old craters with noisy rims as well as young crater with clean rims and so on). At this moment, no real-time hardware-in-the-loop experiment has been conducted to demonstrate the applicability of this technology to space mission. The main objective of the current study is to develop autonomous vision-based navigation algorithms that provide absolute position and surface-relative velocity during the proximity operations of a planetary mission (orbiting phase and landing phase) using a combined approach of TRRN and TRAN technologies. The contributions of the study are: (1) reference mission definition, (2) advancements in the TRAN theory (image processing as well as state estimation) and (3) practical implementation of vision-based navigation.Résumé: L’intérêt des principales agences spatiales envers les technologies basées sur la vision artificielle ne cesse de croître. En effet, les caméras offrent une solution efficace pour répondre aux exigences de performance, toujours plus élevées, des missions spatiales. De surcroît, ces capteurs sont multi-usages, légers, éprouvés et peu coûteux. Plusieurs chercheurs dans le domaine de la vision artificielle se concentrent actuellement sur les systèmes autonomes pour l’atterrissage de précision sur des planètes et sur les missions d’échantillonnage sur des astéroïdes. En effet, sans système de positionnement global « Global Positioning System (GPS) » ou de balises radio autour de ces corps célestes, la navigation de précision est une tâche très complexe. La plupart des systèmes de navigation sont basés seulement sur l’intégration des mesures provenant d’une centrale inertielle. Cette stratégie peut être utilisée pour suivre les mouvements du véhicule spatial seulement sur une courte durée, car les données estimées divergent rapidement. Dans le but d’améliorer la précision de la navigation, plusieurs auteurs ont proposé de fusionner les mesures provenant de la centrale inertielle avec des mesures d’images du terrain. Les premiers algorithmes de navigation utilisant l’imagerie du terrain qui ont été proposés reposent sur l’extraction et le suivi de traits caractéristiques dans une séquence d’images prises en temps réel pendant les phases d’orbite et/ou d’atterrissage de la mission. Dans ce cas, les traits caractéristiques de l’image correspondent à des pixels ayant une forte probabilité d’être reconnus entre des images prises avec différentes positions de caméra. En détectant et en suivant ces traits caractéristiques, le déplacement relatif du véhicule (la vitesse) peut être déterminé. Ces techniques, nommées navigation relative, utilisent des algorithmes de traitement d’images robustes, faciles à implémenter et bien développés. Bien que cette technologie a été éprouvée sur du matériel de qualité spatiale, le gain en précision demeure limité étant donné que la position absolue du véhicule n’est pas observable dans les mesures extraites de l’image. Les techniques de navigation basées sur la vision artificielle actuellement étudiées consistent à identifier des traits caractéristiques dans l’image pour les apparier avec ceux contenus dans une base de données géo-référencées de manière à fournir une mesure de position absolue au filtre de navigation. Cependant, cette technique, nommée navigation absolue, implique l’utilisation d’algorithmes de traitement d’images très complexes souffrant pour le moment des problèmes de robustesse. En effet, ces algorithmes dépendent souvent de la position et de l’attitude du véhicule. Ils sont très sensibles aux conditions d’illuminations (l’élévation et l’azimut du Soleil présents lorsque la base de données géo-référencée est construite doit être similaire à ceux observés pendant la mission). Ils sont grandement influencés par le bruit dans l’image et enfin ils supportent mal les multiples variétés de terrain rencontrées pendant la même mission (le véhicule peut survoler autant des zones de plaine que des régions montagneuses, les images peuvent contenir des vieux cratères avec des contours flous aussi bien que des cratères jeunes avec des contours bien définis, etc.). De plus, actuellement, aucune expérimentation en temps réel et sur du matériel de qualité spatiale n’a été réalisée pour démontrer l’applicabilité de cette technologie pour les missions spatiales. Par conséquent, l’objectif principal de ce projet de recherche est de développer un système de navigation autonome par imagerie du terrain qui fournit la position absolue et la vitesse relative au terrain d’un véhicule spatial pendant les opérations à basse altitude sur une planète. Les contributions de ce travail sont : (1) la définition d’une mission de référence, (2) l’avancement de la théorie de la navigation par imagerie du terrain (algorithmes de traitement d’images et estimation d’états) et (3) implémentation pratique de cette technologie