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Valid risk stratification for carotid atherosclerotic plaques represents a crucial public health issue toward preventing fatal cerebrovascular events. Although motion analysis (MA) provides useful information about arterial wall dynamics, the identification of motion-based risk markers remains a significant challenge. Considering that the ability of a motion estimator (ME) to handle changes in the appearance of motion targets has a major effect on accuracy in MA, we investigated the potential of adaptive block matching (ABM) MEs, which consider changes in image intensities over time. To assure the validity in MA, we optimized and evaluated the ABM MEs in the context of a specially designed in silico framework. ABMFIRF2, which takes advantage of the periodicity characterizing the arterial wall motion, was the most effective ABM algorithm, yielding a 47% accuracy increase with respect to the conventional block matching. The in vivo application of ABMFIRF2 revealed five potential risk markers: low movement amplitude of the normal part of the wall adjacent to the plaques in the radial (RMAPWL) and longitudinal (LMAPWL) directions, high radial motion amplitude of the plaque top surface (RMAPTS), and high relative movement, expressed in terms of radial strain (RSIPL) and longitudinal shear strain (LSSIPL), between plaque top and bottom surfaces. The in vivo results were reproduced by OFLK(WLS) and ABMKF-K2, MEs previously proposed by the authors and with remarkable in silico performances, thereby reinforcing the clinical values of the markers and the potential of those MEs. Future in vivo studies will elucidate with confidence the full potential of the markers. [ABSTRACT FROM AUTHOR]

In this paper, a fully automatic active-contour-based segmentation method is presented, for detecting the carotid artery wall in longitudinal B-mode ultrasound images. A Hough-transform-based methodology is used for the definition of the initial snake, followed by a gradient vector flow (GVF) snake deformation for the final contour detection. The GVF snake is based on the calculation of the image edge map and the calculation of GVF field which guides its deformation for the estimation of the real arterial wall boundaries. In twenty cases there was no significant difference between the automated segmentation and the manual diameter measurements. The sensitivity, specificity and accuracy were 0.97, 0.99 and 0.98, respectively, for both diastolic and systolic cases. In conclusion, the proposed methodology provides an accurate and reliable way to segment ultrasound images of the carotid artery.