• Lilla Bonanno, Fabrizio Sottile, Rosella Ciurleo, Giuseppe Di Lorenzo, Daniele Bruschetta, Alessia Bramanti, Giorgio Ascenti, Placido Bramanti, and Silvia Marino.
• IRCCS Centro Neurolesi "Bonino-Pulejo," Messina, Italy. Electronic address: lillabonanno@gmail.com.
• J Stroke Cerebrovasc Dis. 2017 Feb 1; 26 (2): 411-416.

BackgroundCarotid atherosclerosis is one of the major causes of stroke. The determination of the intima-media thickness, the identification of carotid atherosclerotic plaque, and the classification of the different stenoses are considered as important parameters for the assessment of atherosclerotic diseases. The aim of this work is to segment the plaques and to allow a better comprehension of carotid atherosclerosis.MethodsWe considered 44 subjects, 22 with and 22 without the presence of plaques in the carotid axis, and we applied the snake algorithm.ResultsThe resulting interclass correlation coefficients (ICCs) were significant for all 3 parameters (mean echogenicity: ICC1 = .78 [95%CI: .55-0.90]; perimeter: ICC2 = .81 [95%CI: .61-0.92]; area: ICC3 = .89 [95%CI: .75-0.95]). The diagnostic accuracy was 82%, with an appropriate cutoff value of 224.5, sensitivity of 79%, and specificity of 85%.ConclusionsIn this study, we developed an automatic method to identify the carotid plaque. Our results showed that an automatic system of image segmentation could be used to identify, characterize, and measure atherosclerotic carotid plaques.Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

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