<p>This thesis presents a 3D semi-automatic segmentation technique for extracting the lumen surface of the Carotid arteries including the bifurcation from 3D and 4D ultrasound examinations. </p><p>Ultrasound images are inherently noisy. Therefore, to aid the inspection of the acquired data an adaptive edge preserving filtering technique is used to reduce the general high noise level. The segmentation process starts with edge detection with a recursive and separable 3D Monga-Deriche-Canny operator. To reduce the computation time needed for the segmentation process, a seeded region growing technique is used to make an initial model of the artery. The final segmentation is based on the inflatable balloon model, which deforms the initial model to fit the ultrasound data. The balloon model is implemented with the finite element method. </p><p>The segmentation technique produces 3D models that are intended as pre-planning tools for surgeons. The results from a healthy person are satisfactory and the results from a patient with stenosis seem rather promising. A novel 4D model of wall motion of the Carotid vessels has also been obtained. From this model, 3D compliance measures can easily be obtained.</p>

This thesis describes a set of image analysis tools developed for the purpose of quantifying the distribution of chromatin in (light) microscope images of cell nuclei. The distribution or pattern of chromatin is influenced by both external and internal variations of the cell environment, including variations associated with the cell cycle, neoplasia, apoptosis, and malignancy associated changes (MACs). The quantitative characterisation of this pattern makes possible the prediction of the biological state of a cell, or the detection of subtle changes in a population of cells. This has important application to automated cancer screening. The majority of existing methods for quantifying chromatin distribution (texture) are based on the stochastic approach to defining texture. However, it is the premise of this thesis that the structural approach is more appropriate because pathologists use terms such as clumping, margination, granulation, condensation, and clearing to describe chromatin texture, and refer to the regions of condensed chromatin as granules, particles, and blobs. The key to the structural approach is the segmentation of the chromatin into its texture primitives. Unfortunately all of the chromatin segmentation algorithms published in the literature suffer from one or both of the following drawbacks: (i) a segmentation that is not consistent with a human's perception of blobs, particles, or granules; and (ii) the need to specify, a priori, one or more subjective operating parameters. The latter drawback limits the robustness of the algorithm to variations in illumination and staining quality. The structural model developed in this thesis is based on several novel low-, med-ium-, and high-level image analysis tools. These tools include: a class of non-linear self-dual filters, called folding induced self-dual filters, for filtering impulse noise; an algorithm, based on seeded region growing, for robustly segmenting chromatin; an improved seeded region growing algorithm that is independent of the order of pixel processing; a fast priority queue implementation suitable for implementing the watershed transform (special case of seeded region growing); the adjacency graph attribute co-occurrence matrix (AGACM) method for quantifying blob and mosaic patterns in the plane; a simple and fast algorithm for computing the exact Euclidean distance transform for the purpose of deriving contextual features (measurements) and constructing geometric adjacency graphs for disjoint connected components; a theoretical result establishing an equivalence between the distance transform of a binary image and the grey-scale erosion of its characteristic function by an elliptic poweroid structuring element; and a host of chromatin features that can be related to qualitative descriptions of chromatin distribution used by pathologists. In addition, this thesis demonstrates the application of this new structural model to automated cervical cancer screening. The results provide empirical evidence that it is possible to detect differences in the pattern of nuclear chromatin between samples of cells from a normal Papanicolaou-stained cervical smear and those from an abnormal smear. These differences are supportive of the existence of the MACs phenomenon. Moreover the results compare favourably with those reported in the literature for other stains developed specifically for automated cytometry. To the author's knowledge this is the first time, based on a sizable and uncontaminated data set, that MACs have been demonstrated in Papanicolaou stain. This is an important finding because the primary screening test for cervical cancer, the Papanicolaou test, is based on this stain.

The point cloud is an unorganized set of points with 3D coordinates (x, y, z) which represents a real object. These point clouds are acquired by the technology called 3D scanning. This scanning technique can be done by various methods, such as LIDAR (Light Detection And Ranging) or by utilizing recently developed 3D scanners. Point clouds can be therefore used in various applications, such as mechanical or reverse engineering, rapid prototyping, biology, nuclear physics or virtual reality. Therefore in this doctoral Ph.D. thesis, I focus on feature detection and visualization in a point cloud. These features represent parts of the object that can be described by the well--known mathematical model (lines, planes, helices etc.). The points on the sharp edges are especialy problematic for commonly used methods. Therefore, I focus on detection of these problematic points. This doctoral Ph.D. thesis presents a new algorithm for precise detection of these problematic points. Visualization of these points is done by a modified curve fitting algoritm with a new weight function that leads to better results. Each of the proposed methods were tested on real data sets and compared with contemporary published methods.

Segmentation of an image into individual tree crowns is a key step in the processing of remotely sensed data for forestry practice. The doctoral thesis gives a broad overview of this topic. It comprehends theoretical context from mathematical point of view and defines basic terms from airborne imaging and laser scanning. Mathematical methods of tree detection are focused on a robust adaptation to the actual conditions in a region of interest. A novel approach of crown area delineation is introduced, it combines a seeded region growing technique with an active contour as a crown boundary representation. The parametrisation of all algorithms is analysed in a practical half of the thesis and more application-oriented issues are mentioned. Executable computer programs are attached.

Les maladies cardiovasculaires et en particulier les maladies coronariennes représentent la principale cause de mortalité mondiale avec 17,9 millions de décès en 2012. L’IRM cardiaque est un outil particulièrement intéressant pour la compréhension et l’évaluation des cardiopathies, notamment ischémiques. Son apport diagnostique est souvent majeur et elle apporte des informations non accessibles par d’autres modalités d’imagerie. Les travaux menés pendant cette thèse portent plus particulièrement sur l’examen dit de perfusion myocardique qui consiste à étudier la distribution d’un agent de contraste au sein du muscle cardiaque lors de son premier passage. En pratique clinique cet examen est souvent limité à la seule analyse visuelle du clinicien qui recherche un hyposignal lui permettant d’identifier l’artère coupable et d’en déduire le territoire impacté. Cependant, cette technique est relative et ne permet pas de quantifier le flux sanguin myocardique. Au cours de ces dernières années, un nombre croissant de techniques sont apparues pour permettre cette quantification et ce à toutes les étapes de traitement, depuis l’acquisition jusqu’à la mesure elle-même. Nous avons dans un premier temps établi un pipeline de traitement afin de combiner ces approches et de les évaluer à l’aide d’un fantôme numérique et à partir de données cliniques. Nous avons pu démontrer que l’approche Bayésienne permettait de quantifier la perfusion cardiaque et sa supériorité à évaluer le délai d’arrivé du bolus d’indicateur par rapport au modèle de Fermi. De plus l’approche Bayésienne apporte en supplément des informations intéressantes telles que la fonction de densité de probabilité de la mesure et l’incertitude sur la fonction résidu qui permettent de connaitre la fiabilité de la mesure effectuée notamment en observant la répartition de la fonction de densité de probabilité de la mesure. Enfin, nous avons proposé un algorithme de segmentation des lésions myocardiques, exploitant les dimensions spatiotemporelles des données de perfusion. Cette technique permet une segmentation objective et précise de la région hypoperfusée permettant une mesure du flux sanguin myocardique sur une zone de tissu dont le comportement est homogène et dont la mesure du signal moyen permet une augmentation du rapport contraste à bruit. Sur la cohorte de 30 patients, la variabilité des mesures du flux sanguin myocardique effectuées sur les voxels détectés par cette technique était significativement inférieure à celle des mesures effectuées sur les voxels des zones définies manuellement (différence moyenne=0.14, 95% CI [0.07, 0.2]) et de celles effectuées sur les voxels des zones définies à partir de la méthode bullseye (différence moyenne =0.25, 95% CI [0.17, 0.36]) / Cardiovascular diseases and in particular coronary heart disease are the main cause of death worldwide with 17.9 million deaths in 2012. Cardiac MRI is a particularly interesting tool for understanding and evaluating heart disease, including ischemic heart disease. Its diagnostic contribution is often major and it provides information that is not accessible by other imaging modalities. The work carried out during this thesis focuses more specifically on the so-called myocardium perfusion test, which consists in studying the distribution of a contrast agent within the heart muscle during its first passage. In clinical practice, this examination is often limited to the clinician's visual analysis, allowing him to identify the culprit artery and deduce the impacted territory. However, this technique is relative and does not quantify myocardial blood flow. In recent years, an increasing number of techniques have emerged to enable this quantification at all stages of processing, from acquisition to the measurement itself. We first established a treatment pipeline to combine these approaches and evaluate them using a digital phantom and clinical data. We demonstrated that the Bayesian approach is able to quantify myocardium perfusion and its superiority in evaluating the arrival time of the indicator bolus compared to the Fermi model. In addition, the Bayesian approach provides additional interesting information such as the probability density function of the measurement and the uncertainty of the residual function, which makes it possible to know the reliability of the measurement carried out, in particular by observing the distribution of the probability density function of the measurement. Finally, we proposed an algorithm for segmentation of myocardial lesions, using the spatial and temporal dimensions of infusion data. This technique allows an objective and precise segmentation of the hypoperfused region allowing a measurement of myocardial blood flow over an area of tissue which behavior is homogeneous and which average signal measurement allows an increase in the contrast-to-noise ratio. In the cohort of 30 patients, the variability of myocardial blood flow measurements performed on voxels detected by this technique was significantly lower than that of measurements performed on voxels in manually defined areas (mean difference=0.14, 95% CI[0.07, 0.2]) and those performed on voxels in areas defined using the bullseye method (mean difference=0.25, 95% CI[0.17, 0.36])

This thesis covers with the problems of photogrammetry. It describes the instruments, theoretical background and procedures of acquiring, preprocessing, segmentation of input images and of the depth map calculating. The main content of this thesis is the description of the new method of stereovision. Its algorithm, implementation and evaluation of experiments. The covered method belongs to correlation based methods. The main emphasis lies in the segmentation, which supports the depth map calculation.

This master’s thesis is focused on automating the process of differential leukocyte count in peripherial blood using image processing. It deals with the design of the processing of digital images - from scanning and image preprocessing, segmentation nucleus and cytoplasm, feature selection and classifier, including testing on a set of images that were scanned in the context of this work. This work introduces used segmentation methods and classification procedures which separate nucleus and the cytoplasm of leukocytes. A statistical analysis is performed on the basis of these structures. Following adequate statistical parameters, a set of features has been chosen. This data then go through a classification process realized by three artificial neural networks. Overall were classified 5 types of leukocytes: neutropfiles, lymphocytes, monocytes, eosinophiles and basophiles. The sensitivity and specificity of the classification made for 4 out of 5 leukocyte types (neutropfiles, lymphocytes, monocytes, eosinophiles) is higher than 90 %. Sensitivity of classiffication basophiles was evaluated at 75 % and specificity at 67 %. The total ability of classification has been tested on 111 leukocytes and was approximately 91% successful. All algorithms were created in the MATLAB program.