Research Article| Volume 26, ISSUE 2, P411-416, February 2017

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Automatic Algorithm for Segmentation of Atherosclerotic Carotid Plaque


      Carotid 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.


      We considered 44 subjects, 22 with and 22 without the presence of plaques in the carotid axis, and we applied the snake algorithm.


      The 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%.


      In 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.

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