Common carotid intima-media thickness (IMT) is a reliable measure of early atherosclerosis - its accurate measurement can be used in the process of evaluating the presence and tracking the progression of disease. The aim of this study is to introduce a novel unsupervised Computer Aided Detection (CAD) algorithm that is able to identify and measure the IMT in 2D ultrasound carotid images. The developed technique relies on a suite of image processing algorithms that embeds a statistical model to identify the two interfaces that form the IMT without any user intervention. The proposed image segmentation scheme is based on a spatially continuous vascular model and consists of several steps including data preprocessing, edge filtering, model selection, edge reconstruction and data refinement. To conduct a quantitative evaluation each image was manually segmented by clinical experts and performance metrics between the segmentation results obtained by the proposed method and the ground truth data were calculated. The experimental results show that the proposed CAD system is robust in accurately estimating the IMT in ultrasound carotid data.