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Τίτλος: Prediction of the time period of stroke based on ultrasound image analysis of initially asymptomatic carotid plaques
Συγγραφείς: Kyriacou, Efthyvoulos C. 
Vogazianos, Paris 
Christodoulou, Christodoulos I. 
Loizou, Christos P. 
Panayides, Andreas S. 
Petroudi, Styliani H. 
Pattichis, Marios S. 
Pantziaris, Marios 
Nicolaides, Andrew N. 
Pattichis, Constantinos S. 
Major Field of Science: Engineering and Technology
Field Category: Medical Engineering
Λέξεις-κλειδιά: Predictive models;Support vector machines;Feature extraction;Morphology;Computational modeling;Ultrasonic imaging;Atherosclerosis
Ημερομηνία Έκδοσης: 2015
Πηγή: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, 25-29 August, Milan, Italy
Start page: 334
End page: 337
Conference: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 
Περίληψη: Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size, morphology and texture of the atherosclerotic plaques. Several studies were carried out that demonstrated the usefulness of these feature sets for differentiating between asymptomatic and symptomatic plaques and their corresponding cerebrovascular risk stratification. The aim of this study was to develop predictive modelling for estimating the time period of a stroke event by determining the risk for short term (less or equal to three years) or long term (more than three years) events. Data from 108 patients that had a stroke event have been used. The information collected included clinical and ultrasound imaging data. The prediction was performed at base line where patients were still asymptomatic. Several image texture analysis and clinical features were used in order to create a classification model. The different features were statistically analyzed and we conclude that image texture analysis features extracted using Spatial Gray Level Dependencies method had the best statistical significance. Several predictive models were derived based on Binary Logistic Regression (BLR) and Support Vector Machines (SVM) modelling. The best results were obtained with the SVM modelling models with an average correct classifications score of 77±7% for differentiating between stroke event occurrences within 3 years versus more than 3 years. Further work is needed in investigating additional multiscale texture analysis features as well as more modelling techniques on more subjects.
URI: https://hdl.handle.net/20.500.14279/23606
ISBN: 978-1-4244-9271-8
DOI: 10.1109/EMBC.2015.7318367
Rights: © IEEE
Type: Conference Papers
Affiliation: Frederick University 
University of Cyprus 
Intercollege 
Imperial College London 
University of New Mexico 
Cyprus Institute of Neurology and Genetics 
Cyprus Cardiovascular Disease and Educational Research Trust 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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