Vessel Axis Tracking Using Topology Constrained Surface Evolution

An approach to three-dimensional vessel axis tracking based on surface evolution is presented. The main idea is to guide the evolution of the surface by analyzing its skeleton topology during evolution, and imposing shape constraints on the topology. For example, the intermediate topology can be processed such that it represents a single vessel segment, a bifurcation, or a more complex vascular topology. The evolving surface is then re-initialized with the newly found topology. Re-initialization is a crucial step since it creates probing behavior of the evolving front, encourages the segmentation process to extract the vascular structure of interest and reduces the risk on leaking of the curve into the background. The method was evaluated in two computed tomography angiography applications: (i) extracting the internal carotid arteries including the region in which they traverse through the skull base, which is challenging due to the proximity of bone structures and overlap in intensity values, and (ii) extracting the carotid bifurcations including many cases in which they are severely stenosed and contain calcifications. The vessel axis was found in 90% (18/20 internal carotids in ten patients) and 70% (14/20 carotid bifurcations in a different set of ten patients) of the cases

Vessel tractography using an intensity based tensor model with branch detection

In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert

Vessel tractography using an intensity based tensor model

In the last decade, CAD (Coronary Artery Disease) has been the leading cause of death worldwide [1]. Extraction of arteries is a crucial step for accurate visualization, quantification, and tracking of pathologies. However, coronary artery segmentation is one of the most challenging problems in medical image analysis, since arteries are complex tubular structures with bifurcations, and have possible pathologies. Moreover, appearance of blood vessels and their geometry can be perturbed by stents, calcifications and pathologies such as stenosis. Besides, noise, contrast and resolution artifacts can make the problem more challenging. In this thesis, we present a novel tubular structure segmentation method based on an intensity-based tensor that fits to a vessel, which is inspired from diffusion tensor image (DTI) modeling. The anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized with a single seed point and it is capable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrate the performance of our algorithm on 3 complex tubular structured synthetic datasets, and on 8 CTA (Computed Tomography Angiography) datasets (from Rotterdam Coronary Artery Algorithm Evaluation Framework) for quantitative validation. Additionally, extracted arteries from 10 CTA volumes are qualitatively evaluated by a cardiologist expert's visual scores

Blood Vessel Segmentation and shape analysis for quantification of Coronary Artery Stenosis in CT Angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm

Etude morphologique et métrologique des sinus de Valsalva par traitement d'images tomographiques

L'objectif de cette thèse est l'élaboration et l'application de traitements d'images pour permettre une étude objective et fiable des sinus de Valsalva, importantes cavités de la base de l'aorte. Les méthodes proposées s'appliquent aux séquences ciné-IRM et aux examens de scanner sans qu'il n'y ait à modifier le paramétrage entre deux examens. Pour cela, nous avons d'abord étudié la morphologie de cette zone anatomique puis détaillé les différentes propriétés communes à toutes les images de sinus. Ceux-ci font en l'occurrence partie des principaux organes clairs et peu mobiles. Nous avons donc développé un algorithme qui détecte ces éléments et caractérise chacun d'entre eux par une trajectoire unique. Divers outils de morphologie mathématique ont été utilisés à cette occasion, tout comme pour l'extraction du contour des sinus dans chaque image. L'étape de segmentation repose elle sur la reconstruction géodésique, qui s'avère plus efficace et surtout plus robuste que l'usage de contours actifs usuels. L'intérieur des sinus forme un domaine simplement connexe et étoilé. Grâce à ce postulat, nous avons conçu une nouvelle reconstruction, nommée transformée en aurore, qui limite la propagation des intensités aux supports radiaux et présente les résultats dans un repère polaire pour une meilleure lecture des contours.Les points caractéristiques des sinus ont également été détectés, par étude de rayons et détermination de points dominants. Ces points fournissent les éléments nécessaires à une mesure automatique des sinus, mesure cohérente avec les mesures actuellement réalisées manuellement et les variations intra et inter-observateurs de celles-ci. D'autres outils sont enfin esquissés pour modéliser le contour par coniques, classer les images d'examens cinétiques en fonction du moment du cycle et suivre le mouvement des valves dans ces mêmes examens.L'ensemble de ces travaux ont amené à la réalisation d'un logiciel d'aide au diagnostic qui intègre nos méthodes et dont l'interface est également présentée dans le présent mémoire.This Phd thesis deals with the design and the use of image processing tools in order to allow a reliable and objective study of the sinuses of Valsalva which are important cavities of the aortic root. The proposed methods can be applied on cine-MR sequences and CT examinations without any change in the settings between two examinations.Firstly, we studied the morphology of this anatomical area and its constant properties in all images of the dataset. Sinuses are one of the main bright organs with limited movements. Hence a new algorithm has been designed. It detects and characterizes each bright organ by a single trajectory. Various tools of mathematical morphology are used for this step, as for the extraction of the contour of the sinuses in each image.The segmentation step is based on the geodesic reconstruction, which is more effective and more robust than the usual active contours. The shape depicting the sinuses is simply connected and a star domain. With this assumption, a new reconstruction is proposed, called the Aurora transform. This transform limits the spread of intensities only on the radial lines and shows its results in a polar space for a better reading of edges.The relevant points of the sinuses are also detected by a study of radii and the determination of dominant points along edges. An automatic measurement of the sinuses is deduced from these points. The values are very close to the manual measures currently done according to the intra-and inter-observer variations.Some other tools are finally outlined. They includes the modeling of edges by conics, the image classification depending on the time of the cycle in sequences and the tracking of the aortic valves in these examinations.This work led to the devlopement of a diagnostic aid software based on our methods. Its interface is also presented herein.DIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Blood vessel segmentation and shape analysis for quantification of coronary artery stenosis in CT angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Insight into Carotid Atherosclerotic Plaque Development with CT Angiography

Stroke is a leading cause of mortality and morbidity. Atherosclerotic disease of the carotid arteries is an important cause of ischemic stroke. The general aim of this thesis is to contribute to the knowledge on the pathophysiology of atherosclerosis by means of imaging of the atherosclerotic carotid plaque in vivo. This thesis focusses on: - Quantification of imaging biomarkers of carotid atherosclerotic disease with CT angiography; - The investigation on the role of carotid plaque surface (i.e. ulceration) as an imaging biomarker of plaque instability; - The study of plaque development and its determinants using serial CTA imaging