Intra-plaque neovascularization and inflammation are considered as important indicators of plaque vulnerability, which when ruptured, may cause stroke or acute myocardial infarction. The purpose of this research was to validate and evaluate a semi-automatic method, which allows quantification of carotid plaque neovascularization using contrast-enhanced ultrasound cines, thus enabling assessment of plaque vulnerability. The method detects contrast clusters in the images, and tracks them, to generate over time a path that portrays the neovasculature. It classifies the paths as either artifacts or `blood vessels' and reconstructs the 3D arterial tree. Software-based phantom was developed to represent volumetric structures of the carotid lumen, the plaque, and `objects' passing through the intra-plaque neovasculature. These 3D objects, which mimic microbubbles or clusters of microbubbles, were based on original 2D formations, imaged during clinical examinations using contrast-enhanced ultrasound. Within a plaque, several paths were constructed, representing flow inside blood vessels, and several isolated objects were added, representing artifacts. Different paths were generated, classified into 4 groups: separate paths, paths that merge at some point, paths that branch and intersecting paths. The phantom was used to generate sets of cines, which were then processed by the method. The method identified artifacts and different paths, which were then compared to the `true' ones. Sixty-four 'objects' in 16 movies were examined. All of them were detected. 79% of those objects were well tracked and classified to either artifacts or real blood vessels. The results of this study show that the method accurately identifies artifacts and paths, which allows reconstruction of intra-plaque vascular tree and quantification of the plaque neovasculature, which is associated with plaque vulnerability.