Since 2005, our research team has been developing automated techniques for carotid artery (CA) wall segmentation and
intima-media thickness (IMT) measurement. We developed a snake-based technique (which we named CULEX1,2), a
method based on an integrated approach of feature extraction, fitting, and classification (which we named CALEX3), and
a watershed transform based algorithm4. Each of the previous methods substantially consisted in two distinct stages: Stage-I - Automatic carotid artery detection. In this step, intelligent procedures were adopted to automatically
locate the CA in the image frame. Stage-II - CA wall segmentation and IMT measurement. In this second step, the CA distal (or far) wall is segmented in order to trace the lumen-intima (LI) and media-adventitia (MA) boundaries. The distance between
the LI/MA borders is the IMT estimation.
The aim of this paper is the description of a novel and completely automated technique for carotid artery segmentation
and IMT measurement based on an innovative multi-resolution approach.
The carotid intima-media thickness (IMT) is the most used marker for the progression of atherosclerosis and onset of the
cardiovascular diseases. Computer-aided measurements improve accuracy, but usually require user interaction.
In this paper we characterized a new and completely automated technique for carotid segmentation and IMT
measurement based on the merits of two previously developed techniques. We used an integrated approach of intelligent
image feature extraction and line fitting for automatically locating the carotid artery in the image frame, followed by
wall interfaces extraction based on Gaussian edge operator. We called our system - CARES.
We validated the CARES on a multi-institutional database of 300 carotid ultrasound images. IMT measurement bias
was 0.032 ± 0.141 mm, better than other automated techniques and comparable to that of user-driven methodologies.
Our novel approach of CARES processed 96% of the images leading to the figure of merit to be 95.7%. CARES ensured
complete automation and high accuracy in IMT measurement; hence it could be a suitable clinical tool for processing of
large datasets in multicenter studies involving atherosclerosis.pre-
Most of the algorithms for the common carotid artery (CCA) segmentation require human interaction. The aim of this
study is to show a novel accurate algorithm for the computer-based automated tracing of CCA in longitudinal B-Mode
ultrasound images.
One hundred ultrasound B-Mode longitudinal images of the CCA were processed to delineate the region of interest
containing the artery. The algorithm is based on geometric feature extraction, line fitting, and classification. Output of
the algorithm is the tracings of the near and far adventitia layers. Performance of the algorithm was validated against
human tracings (ground truth) and benchmarked with a previously developed automated technique.
Ninety-eight images were correctly processed, resulting in an overall system error (with respect to ground truth) equal to
0.18 ± 0.17 mm (near adventitia) and 0.17 ± 0.24 mm (far adventitia). In far adventitia detection, our novel technique
outperformed the current standard method, which showed overall system errors equal to 0.07 ± 0.07 mm and 0.49 ± 0.27
mm for near and far adventitia, respectively. We also showed that our new technique is quite insensitive to noise and has
performance independent on the subset of images used for training the classifiers.
Superior architecture of this methodology could constitute a general basis for the development of completely automatic
CCA segmentation strategies.
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