Neurological Risk Assessment
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A CAD system for atherosclerotic plaque assessment.
Afonso D, Seabra J, Jasjit S. Suri, Sanches JM.
Conf Proc IEEE Eng Med Biol Soc. August;2012:1008-11. doi: 10.1109/EMBC.2012.6346104.
Abstract: Recently, several atherosclerotic plaque characterization methods were proposed based on plaque morphology assessed through 2D ultrasound. It is of extreme importance to establish an objective quantification measure which allows the physicians to determine the risk of plaque rupture, and thus, of brain stroke. Having these, sometimes complex, measures easily and quickly assessed might prove invaluable for the physician an patient alike. This paper is a first attempt to incorporate such scores in a user-friendly software platform for Computer-aided Diagnosis. This tool provides a way to objectively and interactively characterize the atherosclerotic plaque, to store relevant patient data and to use several processing tools to outline the plaque and compute different echogenicity measures. Combinations of these features are used to provide two objective measure with clinical significance, known as activity index and enhanced activity index.
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Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment.
Acharya UR, Mookiah MR, Vinitha Sree S, Afonso D, Sanches J, Shafique S, Nicolaides A, Pedro LM, Fernandes E Fernandes J, Suri JS.
Medical & Biological Engineering & Computing. May;2013:51(5):513-23. doi: 10.1007/s11517-012-1019-0. Epub 2013 Jan 6.
Abstract: In the case of carotid atherosclerosis, to avoid unnecessary surgeries in asymptomatic patients, it is necessary to develop a technique to effectively differentiate symptomatic and asymptomatic plaques. In this paper, we have presented a data mining framework that characterizes the textural differences in these two classes using several grayscale features based on a novel combination of trace transform and fuzzy texture. The features extracted from the delineated plaque regions in B-mode ultrasound images were used to train several classifiers in order to prepare them for classification of new test plaques. Our CAD system was evaluated using two different databases consisting of 146 (44 symptomatic to 102 asymptomatic) and 346 (196 symptomatic and 150 asymptomatic) images. Both these databases differ in the way the ground truth was determined. We obtained classification accuracies of 93.1 and 85.3 %, respectively. The techniques are low cost, easily implementable, objective, and non-invasive. For more objective analysis, we have also developed novel integrated indices using a combination of significant features.