Assessment of Stroke by Analysing Cartoid Plaque Morphology
E. Kyriacou (Frederick University, Cyprus), C.I. Christodoulou (University of Cyprus, Cyprus), C. Loizou (Intercollege, Cyprus), M.S. Pattichis (University of New Mexico, USA), C.S. Pattichis (University of Cyprus, Cyprus, University of Patras, Greece) and S. Kakkos (Imperial College, UK)
Copyright: © 2009
Stroke is the third leading cause of death in the Western world and a major cause of disability in adults. The objective of this work was to investigate morphological feature extraction techniques and the use of automatic classifiers; in order to develop a computer aided system that will facilitate the automated characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. Through this chapter we summarize the recent advances in ultrasonic plaque characterization and evaluate the efficacy of computer aided diagnosis based on neural and statistical classifiers using as input morphological features. Several classifiers like the K-Nearest Neighbour(KNN) the Probabilistic Neural Network(PNN) and the Support Vector Machine(SVM) were evaluated resulting to a diagnostic accuracy up to 73.7%.
High-resolution ultrasound has made possible the noninvasive visualization of the carotid bifurcation and for that reason it has been extensively used in the study of arterial wall changes; these include measurement of the thickness of the intima media complex (IMT), estimation of the severity of stenosis due to atherosclerotic plaques and plaque characterization (Reilly, 1983; El-Barghouti, 1996; Elatrozy, 1998). Applications of carotid bifurcation ultrasound include: (1) identification and grading of stenosis of extracranial carotid artery disease often responsible for ischemic strokes, transient ischemic attacks (TIAs) or amaurosis fugax (AF); (2) Follow-up after carotid endarterectomy; (3) evaluation of pulsatile neck mass; (4) investigation of asymptomatic neck bruits: severe internal carotid artery stenosis is a predictive factor for future stroke; (5) cardiovascular risk assessment: the presence of carotid bifurcation atherosclerotic plaques is associated with increased cardiovascular mortality(Joakimsen, 2000; Schmidt, 2003); (6) clinical studies on the effect of lipid-lowering and other medications on carotid intima media thickness(IMT)which includes plaque thickness(Salonen, 2003).
During the last decade, the introduction of computer aided methods and image standardization has improved the objective assessment of carotid plaque echogenicity(El-Barghouti, 1996; Elatrozy, 1998) and heterogeneity(El-Barghouti, 1996; Salonen, 2003) and has largely replaced subjective (visual) assessment(Reilly, 1983; Reilly, 1988) that had been criticized for its relatively poor reproducibility(Arnold, 1987). Through this chapter we are trying to introduce the use of morphological image analysis and automatic classifiers for the creation of an automatic ultrasound image classification system for the estimation of the risk of stroke.
Key Terms in this Chapter
Assessment of the Risk of Stroke: Evaluation of the risk that a person has for a stroke event, based on several risk factors and predictors.
Gray Scale Ultrasound Carotid Plaque Image: Image produced by using B-Mode ultrasound technique on carotid arteries
Automatic Classifiers: Mathematical functions that can classify events based on several features and previously known cases.
Morphology Analysis: Analysis of the morphology of images, describes the structuring elements on an image with no directional sensitivity
Stroke: Rapidly developing loss of brain functions due to malfunction in the blood supply to the brain. This can be due to Ischemia(lack of blood supply) or due to haemorrhage.
Computer Aided Diagnosis: Diagnosis supported by computer methods, usually by using automatic classifiers in order to get an estimation on the exact diagnosis.
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