Computational assessment of atherosclerotic plaque components in MR images

Computational assessment of atherosclerotic plaque components in MR 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 atherosclerotic plaques. In addition, computeraided methods have also been developed for identi cation of features such as echogenicity, texture and surface in such plaques. Also, the identi cation of atherosclerotic plaque components, extraction and analysis of its features, and classi cation of the disease progression is an essential factor to predict the occurence of future cerebral events. Computational algorithms play an important role to automatic segment such components in order to expedite the clinical diagnosis. In this thesis proposal, 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 classi cation used for segmentation, i.e. identi cation, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images, are introduced. This PhD project is intended to propose novel methodologies for improving the existing researches related to the segmentation of atherosclerotic plaque components, extraction of its features and classi cation of the lesion type. Thus, the future tasks to be developed are indicated and summarized.