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The echo intensity reliability was function of the ROI size. Muscle and gender variability in echo intensity was likely due to differences in fibrous and adipose tissue content and distribution. Possible explanations for the observed correlations between muscle echo intensity and subcutaneous layer thickness include the dependence of both variables on total body adiposity or the direct dependence of the extent of intramuscular fat on the amount of subcutaneous fat.

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The intima-media thickness (IMT) of the common carotid artery is a widely used clinical marker of severe cardiovascular diseases. IMT is usually manually measured on longitudinal B-mode ultrasound images. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. Most of these, however, require a certain degree of user interaction. In this paper we describe a new, completely automated layer extraction technique (named CALEXia) for the segmentation and IMT measurement of the carotid wall in ultrasound images. CALEXia is based on an integrated approach consisting of feature extraction, line fitting, and classification that enables the automated tracing of the carotid adventitial walls. IMT is then measured by relying on a fuzzy K-means classifier. We tested CALEXia on a database of 200 images. We compared CALEXia?s performance with those of a previously developed methodology that was based on signal analysis (CULEXsa). Three trained operators manually segmented the images and the average profiles were considered as the ground truth. The average error from CALEXia for lumen-intima (LI) and media- adventitia (MA) interface tracings were 1.46 +/- 1.51 pixel (0.091 +/- 0.093 mm) and 0.40 +/- 0.87 pixel (0.025 +/- 0.055 mm), respectively. The corresponding errors for CULEXsa were 0.55 +/- 0.51 pixels (0.035 +/- 0.032 mm) and 0.59 +/- 0.46 pixels (0.037 +/- 0.029 mm). The IMT measurement error was equal to 0.87 +/- 0.56 pixel (0.054 +/- 0.035 mm) for CALEXia and 0.12 +/- 0.14 pixel (0.01 +/- 0.01 mm) for CULEXsa. Thus, CALEXia showed limited performance in segmenting the LI interface, but outperformed CULEXsa in the MA interface and in the number of images correctly processed (190 for CALEXia and 184 for CULEXsa). Based upon two complementary strategies, we anticipate fusing them for further IMT improvements.

The mean distance errors +/- SD using this integrated approach were 1.05 +/- 1.04 pixels (0.07 +/- 0.07 mm) for proximal or near adventitia and 2.68 +/- 3.94 pixels (0.17 +/- 0.24 mm) for distal or far adventitia. Sixteen of 200 images were not perfectly traced because of the presence of both plaques and blood backscattering. The computational cost ensures the possibility for near real-time detection. Conclusions. Although the CALEXia algorithm automatically detects the CCA, it is also robust and validated over a large database. This can constitute a general basis for a completely automated segmentation procedure widely applicable to other anatomies.