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Thesis deals with ultrasonographic contrast examinations, that are performed to assess tissue perfusion and non-invasive ultrasound method speckle tracking, overcoming the weaknesses of Doppler techniques used to scanning the movement of the tissue.

This thesis addresses the challenge of denoising microscopy images captured under low-light conditionswith varying intensity levels. The study compares three deep learning models — N2V, CARE, andRCAN — against the collaborative filter BM4D, which serves as a reference point. The models weretrained on two distinct datasets: Endoplasmic Reticulum and Mitochondria datasets, both acquired witha lattice light-sheet microscope.Results show that BM4D maintains stable performance metrics and delivers superior visual quality,when compared to the noisy input. In contrast, the deep learning models exhibit poor performance onnoisy test images when trained on datasets with non-uniform noise levels. Additionally, a sensitivitycomparison of neural parameter between the same models was made. Revealing that supervised modelsare data-specific to some extent, whereas the self-supervised N2V demonstrates consistent neuralparameters, suggesting lower data specificity. / Denna uppsats tar upp problemet med att reducera brus i mikroskopibilder tagna under svagaljusförhållanden med varierande intensitetsnivåer. Studien jämför tre djupinlärningsmodeller – N2V,CARE och RCAN – mot det kollaborativa filtret BM4D, vilket agerar som en referenspunkt.Modellerna tränades på två olika dataset: Endoplasmic Reticulum och Mitochondria, båda tagna meden selektiv planbelysningsmikroskop (lattice light-sheet microscope).Resultaten visar att BM4D behåller stabila prestationsmått och levererar bättre visuell kvalitet, jämförtmed den brusiga input. Däremot visar djupinlärningsmodellerna bristande prestanda på brusigatestbilder när de tränats på data med icke-enhetliga brusnivåer. Dessutom gjordes enkänslighetsjämförelse av neurala parametrar mellan samma modeller. Detta visade att de övervakademodellerna är specifika för data i viss utsträckning, medan den självövervakade N2V-modellen visarlika neurala parametrar, vilket tyder på lägre dataspecificitet

<p>This thesis describes the implementation of a real-time, full search, 16x16 bidirectional motion estimation at 24 frames per second with the record performance of 155 Gop/s (1538 ops/pixel) at a high clock rate of 125 MHz. The core of bidirectional motion estimation uses close to 100% FPGA resources with 7 Gbit/s bandwidth to external memory. The architecture allows extremely controlled, macro level floor-planning with parameterized block size, image size, placement coordinates and data words length. The FPGA chip is part of the board that was developed at the Institute of Computer & Communication Networking Engineering, Technical University Braunschweig Germany, in collaboration with Grass Valley Germany in the FlexFilm research project. The goal of the project was to develop hardware and programming methodologies for real-time digital film image processing. Motion estimation core uses FlexWAFE reconfigurable architecture where FPGAs are configured using macro components that consist of weakly programmable address generation units and data stream processing units. Bidirectional motion estimation uses two cores of motion estimation engine (MeEngine) forming main data processing unit for backward and forward motion vectors. The building block of the core of motion estimation is an RPM-macro which represents one processing element and performs 10-bit difference, a comparison, and 19-bit accumulation on the input pixel streams. In order to maximize the throughput between elements, the processing element is replicated and precisely placed side-by-side by using four hierarchal levels, where each level is a very compact entity with its own local control and placement methodology. The achieved speed was further improved by regularly inserting pipeline stages in the processing chain.</p>

This thesis describes the implementation of a real-time, full search, 16x16 bidirectional motion estimation at 24 frames per second with the record performance of 155 Gop/s (1538 ops/pixel) at a high clock rate of 125 MHz. The core of bidirectional motion estimation uses close to 100% FPGA resources with 7 Gbit/s bandwidth to external memory. The architecture allows extremely controlled, macro level floor-planning with parameterized block size, image size, placement coordinates and data words length. The FPGA chip is part of the board that was developed at the Institute of Computer &amp; Communication Networking Engineering, Technical University Braunschweig Germany, in collaboration with Grass Valley Germany in the FlexFilm research project. The goal of the project was to develop hardware and programming methodologies for real-time digital film image processing. Motion estimation core uses FlexWAFE reconfigurable architecture where FPGAs are configured using macro components that consist of weakly programmable address generation units and data stream processing units. Bidirectional motion estimation uses two cores of motion estimation engine (MeEngine) forming main data processing unit for backward and forward motion vectors. The building block of the core of motion estimation is an RPM-macro which represents one processing element and performs 10-bit difference, a comparison, and 19-bit accumulation on the input pixel streams. In order to maximize the throughput between elements, the processing element is replicated and precisely placed side-by-side by using four hierarchal levels, where each level is a very compact entity with its own local control and placement methodology. The achieved speed was further improved by regularly inserting pipeline stages in the processing chain.

Image registration is a fundamental problem that can be found in a diverse range of fields within the research community. It is used in areas such as engineering, science, medicine, robotics, computer vision and image processing, which often require the process of developing a spatial mapping between sets of data. Registration plays a crucial role in the medical imaging field where continual advances in imaging modalities, including MRI, CT and PET, allow the generation of 3D images that explicitly outline detailed in vivo information of not only human anatomy, but also human function. Mutual Information (MI) is a popular entropy-based similarity measure which has found use in a large number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it does not assume the existence of any specific relationship between image intensities. It only assumes a statistical dependence. The basic concept behind any approach using MI is to find a transformation, which when applied to an image, will maximise the MI between two images. This thesis presents research using MI in three major topics encompassed by the computer vision and medical imaging field: rigid image registration, stereo vision, and non-rigid image registration. In the rigid domain, a novel gradient-based registration algorithm (MIGH) is proposed that uses Parzen windows to estimate image density functions and Gauss-Hermite quadrature to estimate the image entropies. The use of this quadrature technique provides an effective and efficient way of estimating entropy while bypassing the need to draw a second sample of image intensities (a procedure required in previous Parzen-based MI registration approaches). It is possible to achieve identical results with the MIGH algorithm when compared to current state of the art MI-based techniques. These results are achieved using half the previously required sample sizes, thus doubling the statistical power of the registration algorithm. Furthermore, the MIGH technique improves algorithm complexity by up to an order of N, where N represents the number of samples extracted from the images. In stereo vision, a popular passive method of depth perception, new extensions have been pro- posed in order to increase the robustness of MI-based stereo matching algorithms. Firstly, prior probabilities are incorporated into the MI measure to considerably increase the statistical power of the matching windows. The statistical power, directly related to the number of samples, can become too low when small matching windows are utilised. These priors, which are calculated from the global joint histogram, are tuned to a two level hierarchical approach. A 2D match surface, in which the match score is computed for every possible combination of template and matching windows, is also utilised to enforce left-right consistency and uniqueness constraints. These additions to MI-based stereo matching significantly enhance the algorithms ability to detect correct matches while decreasing computation time and improving the accuracy, particularly when matching across multi-spectra stereo pairs. MI has also recently found use in the non-rigid domain due to a need to compute multimodal non-rigid transformations. The viscous fluid algorithm is perhaps the best method for re- covering large local mis-registrations between two images. However, this model can only be used on images from the same modality as it assumes similar intensity values between images. Consequently, a hybrid MI-Fluid algorithm is proposed to compute a multimodal non-rigid registration technique. MI is incorporated via the use of a block matching procedure to generate a sparse deformation field which drives the viscous fluid algorithm, This algorithm is also compared to two other popular local registration techniques, namely Gaussian convolution and the thin-plate spline warp, and is shown to produce comparable results. An improved block matching procedure is also proposed whereby a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler is used to optimally locate grid points of interest. These grid points have a larger concentration in regions of high information and a lower concentration in regions of small information. Previous methods utilise only a uniform distribution of grid points throughout the image.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-05-30T17:44:26Z No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-01T11:48:59Z (GMT) No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) / Made available in DSpace on 2017-06-01T11:48:59Z (GMT). No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) Previous issue date: 2015-09-10 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O reconhecimento de ações humanas é um problema desafiador em visão computacional que tem potenciais áreas de aplicações. Para descrever o principal movimento do vídeo um novo descritor de movimento é proposto neste trabalho. Este trabalho combina dois métodos para estimar o movimento entre as imagens: casamento de blocos e de gradiente de intensidade de brilho da imagem. Neste trabalho usa-se um algoritmo de casamento de blocos de tamanho variável para extrair vetores de deslocamento, os quais contém a informação de movimento. Estes vetores são computados em uma sequência de frames obtendo a trajetória do bloco, que possui a informação temporal. Os vetores obtidos através do casamento de blocos são usados para clusterizar as trajetórias esparsas de acordo com a forma. O método proposto computa essa informação para obter tensores de orientação e gerar o descritor final. Este descritor é chamado de autodescritor porque depende apenas do vídeo de entrada. O tensor usado como descritor global é avaliado através da classificação dos vídeos das bases de dados KTH, UCF11 e Hollywood2 com o classificador não linear SVM. Os resultados indicam que este método de trajetórias esparsas é competitivo comparado ao já conhecido método de trajetórias densas, usando tensores de orientação, além de requerer menos esforço computacional. / Human action recognition is a challenging problem in Computer Vision which has many potential applications. In order to describe the main movement of the video a new motion descriptor is proposed in this work. We combine two methods for estimating the motion between frames: block matching and brightness gradient of image. In this work we use a variable size block matching algorithm to extract displacement vectors as a motion information. The cross product between the block matching vector and the gra dient is used to obtain the displacement vectors. These vectors are computed in a frame sequence, obtaining the block trajectory which contains the temporal information. The block matching vectors are also used to cluster the sparse trajectories according to their shape. The proposed method computes this information to obtain orientation tensors and to generate the final descriptor. It is called self-descriptor because it depends only on the input video. The global tensor descriptor is evaluated by classification of KTH, UCF11 and Hollywood2 video datasets with a non-linear SVM classifier. Results indicate that our sparse trajectories method is competitive in comparison to the well known dense tra jectories approach, using orientation tensors, besides requiring less computational effort.

Ce travail de thèse est axé sur le domaine du traitement d'images biomédicales. L'objectif de notre étude est l'estimation des paramètres traduisant les propriétés mécaniques de l'artère carotide in vivo en imagerie échographique, dans une optique de détection précoce de la pathologie cardiovasculaire. L'analyse du mouvement longitudinal des tissus de la paroi artérielle, i.e. dans la même direction que le flux sanguin, représente la motivation majeure de ce travail. Les trois contributions principales proposées dans ce travail sont i) le développement d'un cadre méthodologique original et semi-automatique, dédié à la segmentation et à l'estimation du mouvement de la paroi artérielle dans des séquences in vivo d'images ultrasonores mode-B, ii) la description d'un protocole de génération d'une référence, faisant intervenir les opérations manuelles de plusieurs experts, dans le but de quantifier la précision des résultats de notre méthode malgré l'absence de vérité terrain inhérente à la modalité échographique, et iii) l'évaluation clinique de l'association entre les paramètres mécaniques et dynamiques de la paroi carotidienne et les facteurs de risque cardiovasculaire dans le cadre de la détection précoce de l'athérosclérose. Nous proposons une méthode semi-automatique, basée sur une approche conjointe de segmentation des contours de la paroi et d'estimation du mouvement des tissus. L'extraction de la position des interfaces est réalisée via une approche spécifique à la structure morphologique de la carotide, basée sur une stratégie de programmation dynamique exploitant un filtrage adapté. L'estimation du mouvement est réalisée via une méthode robuste de mise en correspondance de blocs (block matching), basée sur la connaissance du déplacement a priori ainsi que sur la mise à jour temporelle du bloc de référence par un filtre de Kalman spécifique. La précision de notre méthode, évaluée in vivo, correspond au même ordre de grandeur que celle résultant des opérations manuelles réalisées par des experts, et reste sensiblement meilleure que celle obtenue avec deux autres méthodes traditionnelles (i.e. une implémentation classique de la technique de block matching et le logiciel commercial Velocity Vector Imaging). Nous présentons également quatre études cliniques réalisées en milieu hospitalier, où nous évaluons l'association entre le mouvement longitudinal et les facteurs de risque cardiovasculaire. Nous suggérons que le mouvement longitudinal, qui représente un marqueur de risque émergent et encore peu étudié, constitue un indice pertinent et complémentaire aux marqueurs traditionnels dans la caractérisation de la physiopathologie artérielle, reflète le niveau de risque cardiovasculaire global, et pourrait être bien adapté à la détection précoce de l'athérosclérose. / This thesis is focused on the domain of bio-medical image processing. The aim of our study is to assess in vivo the parameters traducing the mechanical properties of the carotid artery in ultrasound imaging, for early detection of cardiovascular diseases. The analysis of the longitudinal motion of the arterial wall tissues, i.e. in the same direction as the blood flow, represents the principal motivation of this work. The three main contributions proposed in this work are i) the development of an original and semi-automatic methodological framework, dedicated to the segmentation and motion estimation of the arterial wall in in vivo ultrasound B-mode image sequences, ii) the description of a protocol aiming to generate a reference, involving the manual tracings of several experts, in the objective to quantify the accuracy of the results of our method despite the absence of ground truth inherent to ultrasound imaging, and iii) the clinical evaluation of the association between the mechanical and dynamical parameters of the arterial wall and the cardiovascular risk factors, for early detection of atherosclerosis. We propose a semi-automatic method, based on a combined approach of wall segmentation and tissues motion estimation. The extraction on the interfaces position is realized via an approach specific to the morphological structure of the carotid artery, based on a strategy of dynamic programming using a matched filter. The motion estimation is performed via a robust block matching method, based on the a priori knowledge of the displacement as well as the temporal update of the reference block with a specific Kalman filter. The accuracy of our method, evaluated in vivo, corresponds to the same order of magnitude as the one resulting from the manual operations performed by experts, and is significantly higher than the one obtained from two other classical methods (i.e. a classical implementation of the block matching technique, and the VVI commercial software). We also present four clinical studies, and we evaluate the association between longitudinal motion and cardiovascular risk factors. We suggest that the longitudinal motion, which represents an emerging cardiovascular risk marker that has been only few studied yet, constitutes a pertinent and complementary marker aiming at the characterization of arterial physio-pathology, traduces the overall cardiovascular risk level, and could be well suited to the early detection of the atherosclerosis.