A review of computational methods applied for identification and quantification of atherosclerotic plaques in images

Evaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed for identification of features such as echogenicity, texture and surface in such plaques. In this article, a review of the most important methodologies that have been developed to identify the main components of atherosclerotic plaques in images is presented. Hence, computational algorithms that take into consideration the analysis of the plaques echogenicity, image processing techniques, clustering algorithms and supervised classification used for segmentation, i.e. identification, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images are introduced. The main contribution of this paper is to provide a categorization of the most important studies related to the segmentation of atherosclerotic plaques and its components in images acquired by the most used imaging modalities. In addition, the effectiveness and drawbacks of each methodology as well as future researches concerning the segmentation and classification of the atherosclerotic lesions are also discussed

Automatic segmentation of cross-sectional coronary arterial images

We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels

Automated image segmentation and registration of vessel wall MRI for quantitative assessment of carotid artery vessel wall dimensions and plaque composition

The main goal of this thesis was to develop methods for automated segmentation, registration and classification of the carotid artery vessel wall and plaque components using multi-sequence MR vessel wall images to assess atherosclerosis. First, a general introduction into atherosclerosis and different stages of the disease were described including the importance to differentiate between stable and vulnerable plaques. Several non-invasive imaging techniques were discussed and the advantages of multi-sequence MRI were highlighted. Different novel automated image segmentation and registration techniques for analysis of the MRI images have been developed. A 3D vessel model to automatically segment the vessel wall was presented. Automated image registration was applied to correct for patient movement during the acquisition of an MRI scan and between MRI scans. The last topic is the automatic classification of the different plaque components which can be present inside the vessel wall. All techniques were developed and validated using relevant patient data and reference standards. The work presented is an important contribution to the automated analysis of multi-sequence MR vessel wall imaging of the carotid artery. These techniques can speed up the current manual analysis and are potentially more accurate and more reproducible.ASCI research school. Bontius Stichting inz. Doelfonds beeldverwerking. Library of the University of Leiden. Medis medical imaging systems bv, Leiden. Pie Medical Imaging BV, MaastrichtUBL - phd migration 201