Segmentation of the Common Carotid Artery Walls Based on a Frequency Implementation of Active Contours

Abstract

Atherosclerosis is one of the most extended cardiovascular diseases nowadays. Although it may be unnoticed during years, it also may suddenly trigger severe illnesses such as stroke, embolisms or ischemia. Therefore, an early detection of atherosclerosis can prevent adult population from suffering more serious pathologies. The intima-media thickness (IMT) of the common carotid artery (CCA) has been used as an early and reliable indicator of atherosclerosis for years. The IMT is manually computed from ultrasound images, a process that can be repeated as many times as necessary (over different ultrasound images of the same patient), but also prone to errors. With the aim to reduce the inter-observer variability and the subjectivity of the measurement, a fully automatic computer-based method based on ultrasound image processing and a frequency-domain implementation of active contours is proposed. The images used in this work were obtained with the same ultrasound scanner (Philips iU22 Ultrasound System) but with different spatial resolutions. The proposed solution does not extract only the IMT but also the CCA diameter, which is not as relevant as the IMT to predict future atherosclerosis evolution but it is a statistically interesting piece of information for the doctors to determine the cardiovascular risk. The results of the proposed method have been validated by doctors, and these results are visually and numerically satisfactory when considering the medical measurements as ground truth, with a maximum deviation of only 3.4 pixels (0.0248 mm) for IMT.

A review of ultrasound common carotid artery image and video segmentation techniques

“…An IMC segmentation approach with initial contour estimation based on a discrete dynamic contour was proposed in [61], which was derived from the entropy image using an initial circle matching procedure. In another study [6], a frequency implementation of active contours was proposed for the segmentation of the artery walls, while in [106], segmentation was achieved utilizing intensity inhomogeneity correction. Finally in [110], an active shape model was used in 3D CCA ultrasound images where large IMT differences with the manual tracings were reported due to weak image edges and speckle noise.…”

Frequency-domain active contours solution to evaluate intima–media thickness of the common carotid artery

“…? Yes <100 Meiburger [33] [39] 2012 AC Yes 95 ± 61.5 No Molinari [35] 2012 AC Yes 21 ± 197 No Bastida [41] 2013 AC Yes -Yes ? Ilea [36] 2013 Modelling Yes 80 ± 40 No Loizou [37] 2013 AC Yes 30 ± 30 Yes 180 ± 150 DP: dynamic programming; AC: active contours; FOAM: first order absolute moment; MSC: mean-shift classifier; EF: edge flow; HT: Hough transform;…”

“…These proposals implement two stages, one to initialize the contours and a second one to segment the artery walls. Following the same idea, other authors have proposed methods based on models [36] or different implementations of active contours [37,38], like active contours without edges [39] based on the Chan-Vese snake [40], or frequency-formulation of active contours [41]. The latter, needs to perform twice the active contour segmentation,increasing the computational cost, and this work lacks the comparison of the results with a ground truth segmentation.…”

Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches

“…Automated image-based methods for carotid lumen segmentation from US images are challenging due to the variability in data sets, such as: shape and size of carotid artery, occlusions caused by multi-focal plaques, arterial curvature, orientation of scanning probe, gain control during the acquisition, type of the transducer (linear vs. phased array), scanning protocol, and frequency of operation [5,8,11,13,14]. Furthermore, the non-uniform plaque growth at the interface of lumen and walls creates a challenge in the segmentation of the carotid artery because the image contrast depends on the composition and grade of the plaque [13].…”