Tato práce se zabývá použitím fázové korelace k určení vzájemné rotace, změny měřítka a posunu mezi digitálními obrazy. Fázová korelace je založena na Fourierově transformaci, proto je popsána Fourierova transformace funkcí definovaných na R^2 i diskrétní Fourierova transformace funkcí definovaných na konečném počtu bodů {0, 1, ... , N-1}^2, kde N je přirozené číslo. Dále je pozornost věnována modifikacím fázové korelace, díky nimž metoda umožňuje nalezení parametrů podobnostní transformace i mezi obrazy, které mají vysoký dynamický rozsah a slabě patrné struktury, obsahují aditivní nebo impulzní šum a jsou pořízeny pomocí různých snímačů a optických soustav. Obsahem práce jsou i modifikace metody pro snímky sluneční koróny pořízené během úplných zatmění Slunce, což patří mezi nejobtížnější úlohy registrace obrazů.

This work is focused on creating 3D maps of indoor enviroment using Microsoft Kinect sensor. The first part shows the description of Microsoft Kinect sensor, the methods for acquisition and processing of depth data and their registration using different algorithms. The second part shows application of algorithms for map registration and final 3D maps of indoor enviroment.

Tato diplomová práce popisuje metodu využití lícování obrazů pro korekci geometrické distorze obrazů pravidelných krystalických struktur získaných z transmisního elektronového mikroskopu (TEM). Cílem této práce je vytvořit algoritmus v Matlabu, který dokáže tyto vady eliminovat nalezením prostorové transformace, která nalícuje zkreslený obraz na jeho modelovou mřížku. Transformace je hledána s využitím optimalizačních metod, které optimalizují zvolenou kriteriální funkci.

Research conducted in the aviation industry includes two major areas, increased safety and a reduction of the environmental footprint. This thesis investigates the possibilities of increased situational awareness with computer vision in avionics systems. Image fusion methods are evaluated with appropriate pre-processing of three image sensors, one in the visual spectrum and two in the infra-red spectrum. The sensor setup is chosen to cope with the different weather and operational conditions of an aircraft, with a focus on the final approach and landing phases. Extensive image quality assessment metrics derived from a systematic review is applied to provide a precise evaluation of the image quality of the fusion methods. A total of four image fusion methods are evaluated, where two are convolutional network-based, using the networks for feature extraction in the detailed layers. Other approaches with visual saliency maps and sparse representation are also evaluated. With methods implemented in MATLAB, results show that a conventional method implementing a rolling guidance filter for layer separation and visual saliency map provides the best results. The results are further confirmed with a subjective ranking test, where the image quality of the fusion methods is evaluated further.

In order to analyse body composition of MRI (Magnetic Resonance Imaging) volumes, atlas-based segmentation is often used to retrieve information from specific organs or anatomical regions. The method behind this technique is to use an already segmented image volume, an atlas, to segment a target image volume by registering the volumes to each other. During this registration a deformation field will be calculated, which is applied to a segmented part of the atlas, resulting in the same anatomical segmentation in the target. The drawback with this method is that the quality of the segmentation is highly dependent on the similarity between the target and the atlas, which means that many atlases are needed to obtain good segmentation results in large sets of MRI volumes. One potential solution to overcome this problem is to create the deformation field between a target and an atlas as a sequence of small deformations between more similar bodies.  In this master thesis a new method for atlas-based segmentation has been developed, with the anticipation of obtaining good segmentation results regardless of the level of similarity between the target and the atlas. In order to do so, 4000 MRI volumes were used to create a manifold of human bodies, which represented a large variety of different body types. These MRI volumes were compared to each other and the calculated similarities were saved in matrices called similarity models. Three different similarity measures were used to create the models which resulted in three different versions of the model. In order to test the hypothesis of achieving good segmentation results when the deformation field was constructed as a sequence of small deformations, the similarity models were used to find the shortest path (the path with the least dissimilarity) between a target and an atlas in the manifold.  In order to evaluate the constructed similarity models, three MRI volumes were chosen as atlases and 100 MRI volumes were randomly picked to be used as targets. The shortest paths between these volumes were used to create the deformation fields as a sequence of small deformations. The created fields were then used to segment the anatomical regions ASAT (abdominal subcutaneous adipose tissue), LPT (left posterior thigh) and VAT (visceral adipose tissue). The segmentation performance was measured with Dice Index, where segmentations constructed at AMRA Medical AB were used as ground truth. In order to put the results in relation to another segmentation method, direct deformation fields between the targets and the atlases were also created and the segmentation results were compared to the ground truth with the Dice Index. Two different types of transformation methods, one non-parametric and one affine transformation, were used to create the deformation fields in this master thesis. The evaluation showed that good segmentation results can be achieved for the segmentation of VAT for one of the constructed similarity models. These results were obtained when a non-parametric registration method was used to create the deformation fields. In order to achieve similar results for an affine registration and to improve the segmentation of other anatomical regions, further investigations are needed.

Airway hyperresponsiveness (AHR) is a hallmark of asthma where constriction of airway smooth muscle (ASM) causes excessive airway narrowing. Asthmatics, unlike healthy subjects, cannot prevent or reverse this narrowing by stretching their airways with a deep inspiration (DI). Since stretching of isolated ASM causes dramatic reductions in force generation and asthmatics tend to have stiffer airways, researchers hypothesize that reduced ASM stretching during breathing and DIs results in hyperreactive airways. However, counterintuitively, excised measurement on intact airways show narrowing is minimally reversed by pressure oscillations simulating breathing and DIs. We hypothesized that AHR does not result from reduced capacity to stretch the airways; furthermore, each constituent of the airway wall experiences different strain magnitude during breathing and DIs. To test this, we used an intact airway system which controls transmural pressure (Ptm) to simulate breathing while measuring luminal diameter in response to ASM agonists. An ultrasound system and automated segmentation algorithm were implemented to quantify and compare the ability of Ptm fluctuations to reverse and prevent narrowing in larger (diameter=5.72±0.52mm) relative to smaller airways (diameter=2.92±0.29mm). We found the ability of Ptm oscillations to reverse airway narrowing was proportional to strain imposed on the airway wall. Further, tidal-like breathing Ptm oscillations (5-15cmH2O) after constriction imposed 196% more strain in smaller compared to larger airways (14.6% vs. 5.58%), resulting in 76% greater reversal of narrowing (41.2% vs. 23.4%). However, Ptm oscillations applied before and during constriction resulted in the same steady-state diameter as when Ptm oscillations were applied only after constriction. To better understand these results, we optimized an ultrasound elastography technique utilizing finite element-based image registration to estimate spatial distributions of displacements, strains, and material properties throughout an airway wall during breathing and bronchoconstriction. This required we formulate and solve an inverse elasticity problem to reconstruct the distribution of nonlinear material properties. Strains and material properties were radially and longitudinally heterogeneous, and patterns and magnitudes changed significantly after induced narrowing. Taken together, these data show AHR likely does not emerge due to reduced straining of airways prior to challenge, but remodeling that stiffens airway walls might serve to sustain constriction during an asthmatic-like attack.

Dénommée multiparamétrique par adjonction de séquences fonctionnelle aux conventionnelles, L’IRM de prostate a montré ses performances pour la détection du cancer de prostate par un score radiologique visuel, subjectif. D’autres applications sont en cours d’investigations comme la stratification, le planning thérapeutique ou encore le suivi oncologique.La première partie s’attache à décrire, élaborer et appliquer une méthodologie de recalage non rigide en 3D entre l’histologie du spécimen de prostatectomie totale et les différentes séquences de l’IRMmp. Après avoir capturé une déformation et un changement de volume de la prostate entre les états in vivo et ex vivo par IRM, la méthode de recalage multimodalité appliquée à une population de prostatectomie totale précédée d’une IRM démontre une sous-estimation du volume de cancer par l’IRM, sujette à une stratification. Les implications se trouvent dans la détection, la stratification et le planning thérapeutique. La deuxième partie propose une analyse de texture des différentes séquences et cartographies quantitatives en diffusion et perfusion pour la détection et la stratification du cancer. Cette approche multiparamétrique de « Score d’Entropie » est testée dans une population pilote au moment des biopsies et présente des performances diagnostique pour sélectionner les lésions à biopsier. Ce score d’entropie participe de la stratification du cancer en corrélant positivement avec le score de Gleason et la longueur de cancer biopsique.La troisième partie explore le rôle de l’IRM dans le suivi d’une thérapie émergente, dite focale, du cancer. Il s’agit d’un travail de recalage non-rigide longitudinal sur une cohorte de patients traités par thérapie focale en vue de compenser les déformation focalement induites. Il apparaît que ce type de recalage peut permettre un suivi objectif des résultats d’ablation et potentiellement élaborer une cible biopsique et radiologique dans le suivi oncologique. / Conventional prostate MRI, enhanced by diffusion and perfusion sequences, and then named multiparametric, showed high performances for detection of prostate cancer using visual scoring. Indications in stratification, prognosis, treatment planning and follow up are currently under investigations.First part of this work attached itself to describe, elaborate and use a non-rigid image fusion method in 3D between gold standard histology of radical prostatectomy and MRI. Investigations captured the significant differences in shape and volume of in vivo and ex vivo prostate using MRI. The developed multimodality fusion method was applied to a cohort of patients who underwent MRI prior surgery. Results showed a stratified underestimation of cancer volume by MRI. Clinical output resides in detection, stratification and surgical planning.The second part proposed some texture analysis of sequences and quantitative maps. As a multiparametric approach, the Entropy Score is applied in a pilot cohort at time of biopsy and showed some potential usefulness to select MRI targets without compromising detection of significant cancer. By positively correlating with the Gleason Score and the maximal core length of cancer, Entropy Score participates of stratification of cancer.The third part explored application of image registration in the longitudinal follow up of an emergent therapy, said focal (FT). As a conservative approach, FT induces very local deformation of the gland which appears to be appropriately modelled by non-rigid registration, then opening possibilities to guide further control biopsy and radiologic assessment.

There is an emerging global epidemic of obesity and related complications, such as type 2diabetes (T2D). Alterations in body composition (adipose tissue, muscle volume and fatcontents) are known to be associated with an increased metabolic risk. Understanding of theunderlying mechanisms is key for development of novel intervention strategies. One study investigating the effect on body composition by different diets is Lipogain1. In this study, it was found that a small weight gain induced by polyunsaturated fats (PUFA, n=19) or saturated fats (SFA, n=20) had very different effects on body fat, liver fat and lean tissue mass respectively. The SFA group gained more liver fat and fat mass in general, while the PUFA group gained more muscle mass. These results were determined by magnetic resonance imaging.  The goal of this project was to visualize the results from Lipogain1 by utilizing the noveltechnique Imiomics. Imiomics is a method for statistical analysis of whole-body medical images. By utilizing image registration, all images are transformed to a common reference space. This enables point-wise comparisons between all images included in the analysis. In this project, mean images of the alterations in fat content and local volume change of the two groups were created. These were used to visualize the alterations in body composition from the study. Additionally, statistical tests were used to visualize statistically significant differences between the groups.  Differences between the groups could be seen in the mean images. Mainly a higher fat content increase was seen in SFA in comparison to PUFA. There was also a larger volume expansion in fat tissue in SFA than in PUFA, while PUFA instead had a larger volume expansion in muscles. An unexpected result was also found; the liver had expanded in PUFA but not in SFA. Unfortunately, few significant differences could be visualized between the groups when the statistical test was performed. The conclusion was that this method is promising for visualization of these kinds of studies, especially due to the potential of finding new, unexpected results. However, a somewhat larger cohort and possibly larger alterations in body composition might be needed to be able to visualize and quantify statistically significant differences between the groups on a voxel-wise level.

Cette thèse commence par une étude approfondie des méthodes d’estimation de décalage sous-pixeliques rapides. Une comparaison complète est effectuée prenant en compte problèmes d’estimation de décalage existant dans des applications réelles, à savoir, avec différentes conditions de SNR, différentes grandeurs de déplacement, la non préservation de la contrainte de luminosité constante, l’aliasing et, surtout, la limitation des ressources de calcul. Sur la base de cette étude, en collaboration avec le CNES (l’agence spatiale française), deux problèmes qui sont cruciaux pour l’optique numérique des satellites d’observation de la terre sont analysés. Nous étudions d’abord le problème de correction de front d’onde dans le contexte de l’optique actif. Nous proposons un algorithme pour mesurer les aberrations de front d’onde sur un senseur de type Shack-Hartmann (SHWFS en anglais) en observant la terre. Nous proposons ici une revue de l’état de l’art des méthodes pour le SHWFS utilisé sur des scènes étendues (comme la terre) et concevons une nouvelle méthode pour améliorer l’estimation de front d’onde, en utilisant une approche basée sur l’équation du flot optique. Nous proposons également deux méthodes de validation afin d’assurer une estimation correcte du front d’onde sur les scènes étendues. Tandis que la première est basée sur une adaptation numérique des bornes inférieures (théoriques) pour le recalage d’images, la seconde méthode défausse rapidement les paysages en se basant sur la distribution des gradients. La deuxième application de satellite abordée est la conception numérique d’une nouvelle génération de senseur du type Time Delay Integration (TDI). Dans ce nouveau concept, la stabilisation active en temps réel du TDI est réalisée pour étendre considérablement le temps d’intégration, et donc augmenter le RSB des images. Les lignes du TDI ne peuvent pas être fusionnées directement par addition parce que leur position est modifiée par des microvibrations. Celles-ci doivent être compensées en temps réel avec une précision sous-pixellique. Nous étudions les limites fondamentales théoriques de ce problème et proposons une solution qui s’en approche. Nous présentons un système utilisant la convolution temporelle conjointement à une estimation en ligne du bruit de capteur, à une estimation de décalage basée sur les gradients et à une méthode multiimage non conventionnelle pour mesurer les déplacements globaux. Les résultats obtenus sont concluants sur les fronts de la précision et de la complexité. Pour des modèles de transformation plus complexes, une nouvelle méthode effectuant l’estimation précise et robuste des modèles de mise en correspondance des points d’intérêt entre images est proposée. La difficulté provenant de la présence de fausses correspondances et de mesures bruitées conduit à un échec des méthodes de régression traditionnelles. En vision par ordinateur, RANSAC est certainement la méthode la plus utilisée pour surmonter ces difficultés. RANSAC est capable de discriminer les fausses correspondances en générant de façon aléatoire des hypothèses et en vérifiant leur consensus. Cependant, sa réponse est basée sur la seule itération qui a obtenu le consensus le plus large, et elle ignore toutes les autres hypothèses. Nous montrons ici que la précision peut être améliorée en agrégeant toutes les hypothèses envisagées. Nous proposons également une stratégie simple qui permet de moyenner rapidement des transformations 2D, ce qui réduit le coût supplémentaire de calcul à quantité négligeable. Nous donnons des applications réelles pour estimer les transformations projectives et les transformations homographie + distorsion. En incluant une adaptation simple de LO-RANSAC dans notre cadre, l’approche proposée bat toutes les méthodes de l’état de l’art. Une analyse complète de l’approche proposée est réalisée, et elle démontre un net progrès en précision, stabilité et polyvalence. / This thesis starts with an in-depth study of fast and accurate sub-pixel shift estimationmethods. A full comparison is performed based on the common shift estimation problems occurring in real-life applications, namely, varying SNR conditions, differentdisplacement magnitudes, non-preservation of the brightness constancy constraint, aliasing, and most importantly, limited computational resources. Based on this study, in collaboration with CNES (the French space agency), two problems that are crucial for the digital optics of earth-observation satellites are analyzed.We first study the wavefront correction problem in an active optics context. We propose a fast and accurate algorithm to measure the wavefront aberrations on a Shack-HartmannWavefront Sensor (SHWFS) device observing the earth. We give here a review of state-of-the-art methods for SHWFS used on extended scenes (such as the earth) and devise a new method for improving wavefront estimation, based on a carefully refined approach based on the optical flow equation. This method takes advantage of the small shifts observed in a closed-loop wavefront correction system, yielding improved accuracy using fewer computational resources. We also propose two validation methods to ensure a correct wavefront estimation on extended scenes. While the first one is based on a numerical adaptation of the (theoretical) lower bounds of image registration, the second method rapidly discards landscapes based on the gradient distribution, inferred from the Eigenvalues of the structure tensor.The second satellite-based application that we address is the numerical design of a new generation of Time Delay Integration (TDI) sensor. In this new concept, active real-time stabilization of the TDI is performed to extend considerably the integration time, and therefore to boost the images SNR. The stripes of the TDI cannot be fused directly by addition because their position is altered by microvibrations. These must be compensated in real time using limited onboard computational resources with high subpixel accuracy. We study the fundamental performance limits for this problem and propose a real-time solution that nonetheless gets close to the theoretical limits. We introduce a scheme using temporal convolution together with online noise estimation, gradient-based shift estimation and a non-conventional multiframe method for measuring global displacements. The obtained results are conclusive on the fronts of accuracy and complexity and have strongly influenced the final decisions on the future configurations of Earth observation satellites at CNES.For more complex transformation models, a new image registration method performing accurate robust model estimation through point matches between images is proposed here. The difficulty coming from the presence of outliers causes the failure of traditional regression methods. In computer vision, RANSAC is definitely the most renowned method that overcomes such difficulties. It discriminates outliers by randomly generating minimalist sampled hypotheses and verifying their consensus over the input data. However, its response is based on the single iteration that achieved the largest inlier support, while discarding all other generated hypotheses. We show here that the resulting accuracy can be improved by aggregating all hypotheses. We also propose a simple strategy that allows to rapidly average 2D transformations, leading to an almost negligible extra computational cost. We give practical applications to the estimation of projective transforms and homography+distortion transforms. By including a straightforward adaptation of the locally optimized RANSAC in our framework, the proposed approach improves over every other available state-of-the-art method. A complete analysis of the proposed approach is performed, demonstrating its improved accuracy, stability and versatility.

Indiana University-Purdue University Indianapolis (IUPUI) / Adaptive Optics Optical Coherence Tomography (AO-OCT) is a high-speed, high-resolution ophthalmic imaging technique offering detailed 3D analysis of retina structure in vivo. However, AO-OCT volume images are sensitive to involuntary eye movements that occur even during steady fixation and include tremor, drifts, and micro-saccades. To correct eye motion artifacts within a volume and to stabilize a sequence of volumes acquired of the same retina area, we propose a stripe-wise 3D image registration algorithm with phase correlation. In addition, using several ideas such as coarse-to-fine approach, spike noise filtering, pre-computation caching, and parallel processing on a GPU, our approach can register a volume of size 512 x 512 x 512 in less than 6 seconds, which is a 33x speedup as compared to an equivalent CPU version in MATLAB. Moreover, our 3D registration approach is reliable even in the presence of large motions (micro-saccades) that distort the volumes. Such motion was an obstacle for a previous en face approach based on 2D projected images. The thesis also investigates GPU implementations for 3D phase correlation and 2D normalized cross-correlation, which could be useful for other image processing algorithms.