Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors.

No description available.

Cette thèse traite de la reconstruction tridimensionnelle d’une scène rigide à partir d’une collection de photographies numériques, dites vues. Le problème traité est connu sous le nom du "calcul de la structure et du mouvement" (structure-and/from-motion) qui consiste à "expliquer" des trajectoires de points dits d’intérêt au sein de la collection de vues par un certain mouvement de l’appareil (dont sa trajectoire) et des caractéristiques géométriques tridimensionnelles de la scène. Dans ce travail, nous proposons les fondements théoriques pour étendre certaines méthodes de calcul de la structure et du mouvement afin d’intégrer comme données d’entrée, des points d’intérêt réels et des points d’intérêt complexes, et plus précisément des images de points cycliques. Pour tout plan projectif, les points cycliques forment une paire de points complexes conjugués qui, par leur invariance par les similitudes planes, munissent le plan projectif d’une structure euclidienne. Nous introduisons la notion de marqueurs cycliques qui sont des marqueurs plans permettant de calculer sans ambiguïté les images des points cycliques de leur plan de support dans toute vue. Une propriété de ces marqueurs, en plus d’être très "riches" en information euclidienne, est que leurs images peuvent être appariées même si les marqueurs sont disposés arbitrairement sur des plans parallèles, grâce à l’invariance des points cycliques. Nous montrons comment utiliser cette propriété dans le calcul projectif de la structure et du mouvement via une technique matricielle de réduction de rang, dite de factorisation, de la matrice des données correspondant aux images de points réels, complexes et/ou cycliques. Un sous-problème critique abordé dans le calcul de la structure et du mouvement est celui de l’auto-calibrage de l’appareil, problème consistant à transformer un calcul projectif en un calcul euclidien. Nous expliquons comment utiliser l’information euclidienne fournie par les images des points cycliques dans l’algorithme d’auto-calibrage opérant dans l’espace projectif dual et fondé sur des équations linéaires. L’ensemble de ces contributions est finalement utilisé pour une application de suivi automatique de caméra utilisant des marqueurs formés par des couronnes concentriques (appelés CCTags), où il s’agit de calculer le mouvement tridimensionnel de la caméra dans la scène à partir d’une séquence vidéo. Ce type d’application est généralement utilisé dans l’industrie du cinéma ou de la télévision afin de produire des effets spéciaux. Le suivi de caméra proposé dans ce travail a été conçu pour proposer le meilleur compromis possible entre flexibilité d’utilisation et précision des résultats obtenus. / The thesis deals with the problem of 3D reconstruction of a rigid scene from a collection of views acquired by a digital camera. The problem addressed, referred as the Structure-from-Motion (SfM) problem, consists in computing the camera motion (including its trajectory) and the 3D characteristics of the scene based on 2D trajectories of imaged features through the collection. We propose theoretical foundations to extend some SfM paradigms in order to integrate real as well as complex imaged features as input data, and more especially imaged circular points. Circular points of a projective plane consist in a complex conjugate point-pair which is fixed under plane similarity ; thus endowing the plane with an Euclidean structure. We introduce the notion of circular markers which are planar markers that allows to compute, without any ambiguity, imaged circular points of their supporting plane in all views. Aside from providing a very “rich” Euclidean information, such features can be matched even if they are arbitrarily positioned on parallel planes thanks to their invariance under plane similarity ; thus increasing their visibility compared to natural features. We show how to benefit from this geometric property in solving the projective SfM problem via a rank-reduction technique, referred to as projective factorization, of the matrix whose entries are images of real, complex and/or circular features. One of the critical issues in such a SfM paradigm is the self-calibration problem, which consists in updating a projective reconstruction into an euclidean one. We explain how to use the euclidean information provided by imaged circular points in the self-calibration algorithm operating in the dual projective space and relying on linear equations. All these contributions are finally used in an automatic camera tracking application relying on markers made up of concentric circles (called C2Tags). The problem consists in computing the 3D camera motion based on a video sequence. This kind of application is generally used in the cinema or TV industry to create special effects. The camera tracking proposed in this work in designed in order to provide the best compromise between flexibility of use and accuracy.

Mobile virtual reality (VR) headsets have been becoming more and more popular. However, the cheapest headsets do not come with any controllers and the ones that do include controllers only uses sensors for rotation, not translational movement. This thesis project aims to develop a prototype of a Bluetooth connected controller for the mobile VR headsets. The controller is based on a MetamotionC board produced by mbientlab Inc., which comes with Bluetooth Low Energy (BLE), an ARM M4 microcontroller, an miniature inertial measurement unit (IMU) sensor (containing a 3-axis accelerometer, a 3-axis gyroscope, and a 3-axis magnetometer and a barometer), a thermometer and other sensors. The only sensors used in this project are the accelerometer, gyroscope, and magnetometer. As a finished prototype, the MetaMotionC is placed on a glove together with five Aruco markers; a 3D model of a hand intended to use as an avatar of the glove was made with Blender and MakeHuman; and a VR room to use the controller with was created in Unity. The 3D hand responds to rotational and translational movements via Bluetooth connection to the IMU sensor on the MetaMotionC. The smartphone camera is used to detect the glove's position with Aruco markers, and the 3D hand is moved to a corresponding location in the VR room. The OpenCV library is used for image processing. The sensor data is filtered with low-pass, median, and thresholding to improve the measurement accuracy. Zero velocity update is used to reset the drift of the integrated accelerations. To reduce the integration error, Romberg's method with a floating window is implemented in Matlab. However, it did not reduce the error enough to make a difference. Thus, the result was unreliable.

Multicopters such as quadcopters are a popular tool within industries such as mining, shipping and surveillance where a high level of autonomy can save time, increase efficiency and most importantly provide safety. While Unmanned Aerial Vehicles have been a big area in research and used in the mentioned industries, the level of autonomy is still low. Simple actions such as loading and offloading payload or swapping batteries is still a manual task performed by humans. If multicopters are to be used as an autonomous tool the need for solutions where the machines can perform the simplest task such as swapping batteries become an important stepping stone to reach the autonomy goals. Earlier works propose landing solutions focused on landing autonomous vehicles but the lack of accuracy is hindering the vehicles to safely dock with a landing platform. This thesis combines multiple areas such as trajectory generation, visual marker tracking and UAV control where results are shown in both simulation and laboratory experiments. With the use of a Model Predictive Controller for both trajectory generation and UAV control, a multicopter can safely land on a small enough platform which can be mounted on a small mobile robot. Additionally an algorithm to tune the trajectory generator is presented which shows how much weights can be increased in the MPC controller for the system to remain stable.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24884号 / 医博第5018号 / 新制||医||1068(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 鈴木 実, 教授 小濱 和貴, 教授 中島 貴子 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Ce travail de thèse concerne le traitement d’image fDOT (fDOT pour fluorescence diffuse optical tomography) suit vers deux axes. Le recalage d'images fDOT à l’aide de l’imagerie TEP (tomographie par émission de positons) et l’amélioration des reconstructions fDOT à l’aide de miroirs pour collecter des projections complémentaires. Il est présenté en deux parties : Dans la première partie, une méthode automatique pour recaler les images de fDOT avec les images de Tomographie par Emission de Positons (TEP) développée dans le but de corréler l’ensemble des informations issues de chaque modalité. Cette méthode de recalage est basée sur une détection automatique de marqueurs fiduciaires présents dans les deux modalités. La particularité de cette méthode est l’utilisation de l’image de surface obtenue en fDOT, qui sert à identifier la position en Z des marqueurs fiduciaires dans les images optiques. Nous avons testé cette méthode sur un modèle de souris porteuses de xénogreffes de tumeurs de cellules cancéreuses MEN2A qui imitent un carcinome thyroïdien médullaire humain, après une double injection de traceur radioactif : [18F]2-fluoro-2-Deoxy-D-glucose (FDG) pour l’imagerie TEP et un traceur optique d’infrarouge fluorescent, le Sentidye. Grâce à la précision de notre méthode, nous arrivons à démontrer que le signal Sentidye est présent à la fois dans la tumeur et les vaisseaux environnants [1]. La qualité des images fDOT est dégradée selon l’axe Z du fait d’un nombre limité de projections pour la reconstruction. Dans la deuxième partie, le travail s’est orienté vers une nouvelle méthode de reconstruction d’images fDOT à partir d’un nouveau système d’acquisition multi-angulaire avec deux miroirs placés de chaque côté de l’animal. Ce travail a été mené en collaboration avec le département CS d’University College London (UCL), partenaire du projet Européen FMT-XCT. Le logiciel TOAST développé par cette équipe a été utilisé comme source pour l’algorithme de reconstruction, et modifié pour s’adapter à notre problématique. Après plusieurs essais concernant l’ajustement des paramètres du programme, nous avons appliqué cette méthode sur un fantôme réaliste des tissus biologiques et chez la souris. Les résultats montrent une amélioration de l’image reconstruite d’un fantôme semi-cylindrique et de l’image de rein chez la souris, pour lesquelles la méthode des miroirs est supérieure à la méthode classique sans miroir. Malgré tout, nous avons observé que les résultats étaient très sensibles à certains paramètres, d’où une performance de reconstruction variable d’un cas à l’autre. Les perspectives futures concernent l’optimisation des paramètres afin de généraliser l’approche multi-angle. / This thesis concerns the image processing of fluorescence diffuse optical tomography (fDOT), following two axes: FDOT image co-registration with PET (positron emission tomography) image and improvement of fDOT image reconstructions using mirrors to collect additional projections. It is presented in two parts:In the first part, an automatic method to co-register the fDOT images with PET images has been developed to correlate all the information from each modality. This co-registration method is based on automatic detection of fiducial markers (FM) present in both modalities. The particularity of this method is the use of optical surface image obtained in fDOT imaging system, which serves to identify the Z position of FM in optical images. We tested this method on a model of mice bearing tumor xenografts of MEN2A cancer cells that mimic a human medullary thyroid carcinoma, after a double injection of radiotracer [18F] 2-fluoro-2-Deoxy-D-glucose ( FDG) for PET imaging and optical fluorescent infrared tracer Sentidye. With the accuracy of our method, we can demonstrate that the signal of Sentidye is present both in the tumor and surrounding vessels.The fDOT reconstruction image quality is degraded along the Z axis due to a limited number of projections for reconstruction. In the second part, the work is oriented towards a new method of fDOT image reconstruction with a new multi-angle data acquisition system in placing two mirrors on each side of the animal. This work was conducted in collaboration with the CS Department of University College London (UCL), a partner of the European project FMT-XCT. TOAST software developed by this team was used as source code for the reconstruction algorithm, and was modified to adapt to the concerned problem. After several tests on the adjustment of program parameters, we applied this method on a phantom that simulating the biological tissue and on mice. The results showed an improvement in the reconstructed image of a semi-cylindrical phantom and the image of mouse kidney, for which the reconstruction of the mirrors geometry is better than that of conventional geometry without mirror. Nevertheless, we observed that the results were very sensitive to certain parameters, where the performance of reconstruction varies from one case to another. Future prospectives concern the optimization of parameters in order to generalize the multi-angle approach.

Automated human face recognition is a computer vision problem of considerable practical significance. Existing two dimensional (2D) face recognition techniques perform poorly for faces with uncontrolled poses, lighting and facial expressions. Face recognition technology based on three dimensional (3D) facial models is now emerging. Geometric facial models can be easily corrected for pose variations. They are illumination invariant, and provide structural information about the facial surface. Algorithms for 3D face recognition exist, however the area is far from being a matured technology. In this dissertation we address a number of open questions in the area of 3D human face recognition. Firstly, we make available to qualified researchers in the field, at no cost, a large Texas 3D Face Recognition Database, which was acquired as a part of this research work. This database contains 1149 2D and 3D images of 118 subjects. We also provide 25 manually located facial fiducial points on each face in this database. Our next contribution is the development of a completely automatic novel 3D face recognition algorithm, which employs discriminatory anthropometric distances between carefully selected local facial features. This algorithm neither uses general purpose pattern recognition approaches, nor does it directly extend 2D face recognition techniques to the 3D domain. Instead, it is based on an understanding of the structurally diverse characteristics of human faces, which we isolate from the scientific discipline of facial anthropometry. We demonstrate the effectiveness and superior performance of the proposed algorithm, relative to existing benchmark 3D face recognition algorithms. A related contribution is the development of highly accurate and reliable 2D+3D algorithms for automatically detecting 10 anthropometric facial fiducial points. While developing these algorithms, we identify unique structural/textural properties associated with the facial fiducial points. Furthermore, unlike previous algorithms for detecting facial fiducial points, we systematically evaluate our algorithms against manually located facial fiducial points on a large database of images. Our third contribution is the development of an effective algorithm for computing the structural dissimilarity of 3D facial surfaces, which uses a recently developed image similarity index called the complex-wavelet structural similarity index. This algorithm is unique in that unlike existing approaches, it does not require that the facial surfaces be finely registered before they are compared. Furthermore, it is nearly an order of magnitude more accurate than existing facial surface matching based approaches. Finally, we propose a simple method to combine the two new 3D face recognition algorithms that we developed, resulting in a 3D face recognition algorithm that is competitive with the existing state-of-the-art algorithms. / text

Les maladies cardiovasculaires sont l'une des principales causes de mortalité chez l'homme. Une façon de diagnostiquer des maladies cardiaques et des anomalies est le traitement de signaux cardiaques tels que le ECG. Dans beaucoup de ces traitements, des caractéristiques inter-battements et intra-battements de signaux ECG doivent être extraites. Ces caractéristiques comprennent les points de repère des ondes de l’ECG (leur début, leur fin et leur point de pic), les intervalles significatifs et les segments qui peuvent être définis pour le signal ECG. L'extraction des points de référence de l'ECG consiste à identifier l'emplacement du pic, de début et de la fin de l'onde P, du complexe QRS et de l'onde T. Ces points véhiculent des informations cliniquement utiles, mais la segmentation precise de chaque battement de l'ECG est une tâche difficile, même pour les cardiologues expérimentés.Dans cette thèse, nous utilisons un cadre bayésien basé sur le modèle dynamique d'ECG proposé par McSharry. Depuis ce modèle s'appuyant sur la morphologie des ECG, il peut être utile pour la segmentation et l'analyse d'intervalles d'ECG. Afin de tenir compte de la séquentialité des ondes P, QRS et T, nous utiliserons également l'approche de Markov et des modèles de Markov cachés (MMC). En bref dans cette thèse, nous utilisons un modèle dynamique (filtre de Kalman), un modèle séquentiel (MMC) et leur combinaison (commutation de filtres de Kalman (SKF)). Nous proposons trois méthodes à base de filtres de Kalman, une méthode basée sur les MMC et un procédé à base de SKF. Nous utilisons les méthodes proposées pour l'extraction de points de référence et l'analyse d'intervalles des ECG. Le méthodes basées sur le filtrage de Kalman sont également utilisés pour le débruitage d'ECG, la détection de l'alternation de l'onde T, et la détection du pic R de l'ECG du foetus.Pour évaluer les performances des méthodes proposées pour l'extraction des points de référence de l'ECG, nous utilisons la base de données "Physionet QT", et une base de données "Swine" qui comprennent ECG annotations de signaux par les médecins. Pour le débruitage d'ECG, nous utilisons les bases de données "MIT-BIH Normal Sinus Rhythm", "MIT-BIH Arrhythmia" et "MIT-BIH noise stress test". La base de données "TWA Challenge 2008 database" est utilisée pour la détection de l'alternation de l'onde T. Enfin, la base de données "Physionet Computing in Cardiology Challenge 2013 database" est utilisée pour la détection du pic R de l'ECG du feotus. Pour l'extraction de points de reference, la performance des méthodes proposées sont évaluées en termes de moyenne, écart-type et l'erreur quadratique moyenne (EQM). Nous calculons aussi la sensibilité des méthodes. Pour le débruitage d'ECG, nous comparons les méthodes en terme d'amélioration du rapport signal à bruit. / Cardiovascular diseases are one of the major causes of mortality in humans. One way to diagnose heart diseases and abnormalities is processing of cardiac signals such as ECG. In many of these processes, inter-beat and intra-beat features of ECG signal must be extracted. These features include peak, onset and offset of ECG waves, meaningful intervals and segments that can be defined for ECG signal. ECG fiducial point (FP) extraction refers to identifying the location of the peak as well as the onset and offset of the P-wave, QRS complex and T-wave which convey clinically useful information. However, the precise segmentation of each ECG beat is a difficult task, even for experienced cardiologists.In this thesis, we use a Bayesian framework based on the McSharry ECG dynamical model for ECG FP extraction. Since this framework is based on the morphology of ECG waves, it can be useful for ECG segmentation and interval analysis. In order to consider the time sequential property of ECG signal, we also use the Markovian approach and hidden Markov models (HMM). In brief in this thesis, we use dynamic model (Kalman filter), sequential model (HMM) and their combination (switching Kalman filter (SKF)). We propose three Kalman-based methods, an HMM-based method and a SKF-based method. We use the proposed methods for ECG FP extraction and ECG interval analysis. Kalman-based methods are also used for ECG denoising, T-wave alternans (TWA) detection and fetal ECG R-peak detection.To evaluate the performance of proposed methods for ECG FP extraction, we use the "Physionet QT database", and a "Swine ECG database" that include ECG signal annotations by physicians. For ECG denoising, we use the "MIT-BIH Normal Sinus Rhythm", "MIT-BIH Arrhythmia" and "MIT-BIH noise stress test" databases. "TWA Challenge 2008 database" is used for TWA detection and finally, "Physionet Computing in Cardiology Challenge 2013 database" is used for R-peak detection of fetal ECG. In ECG FP extraction, the performance of the proposed methods are evaluated in terms of mean, standard deviation and root mean square of error. We also calculate the Sensitivity for methods. For ECG denoising, we compare methods in their obtained SNR improvement.

This thesis deals with a proposition of an algorithm for QRS complex and typical ECG waves boundaries detection. It incorporates a literature research focused on heart electrophysiology and commonly used methods for ECG fiducial points detection and delineation. Out of the presented methods an algorithm based on a continuous wavelet transform is implemented. Detection and delineation algorithm is tested on CSE standard signal database towards references determined both manually and automatically. Obtained results are compared to other congenerous methods. The diploma thesis is further concerned with an algorithm modification for experimental electrocardiograms of isolated rabbit hearts. Recording specifics of these data are introduced. Additionally, based on time and frequency analysis, particular modifications of the algorithm are proposed and realized. Due to a large extent of records functionality is verified on randomly selected database samples. Efficiency of the modified algorithm is evaluated through manually annotated references.