Multiscale edge detection and gradient vector flow snakes for automated identification of the carotid artery wall in longitudinal B-mode ultrasound images

This paper proposes a fully automatic active-contour-based segmentation method, for the detection of the carotid artery wall in longitudinal B-mode images. A multiscale edge detection methodology is used for the definition of an initial snake contour, followed by a gradient vector flow (GVF) snake. The multiscale edge detection method is based on finding the local maxima of the wavelet transform of the image, which is very close to the real contour. The GVF snake is based on the calculation of the image edge map and the calculation of the GVF field which guides the deformation for the estimation of the real arterial wall boundaries. In twenty cases of healthy carotid arteries the sensitivity, specificity and accuracy were higher than 0.97, 0.99 and 0.98 respectively, for both diastolic and systolic cases. In conclusion, the proposed methodology provides a new accurate procedure to detect the arterial wall in ultrasound images of the carotid artery.