This article describes a novel method for highly accurate and effective localization of the transverse section of the carotis comunis artery in ultrasound images. The method has a high success rate, approximately 97%. Unlike analytical methods based on geometric descriptions of the object sought, the method proposed here can cover a large area of shape variation of the artery under study, which normally occurs during examinations as a result of the pressure on the examined tissue, tilt of the probe, setup of the sonographic device, and other factors. This method shows great promise in automating the process of determining circulatory system parameters in the non-invasive clinical diagnostics of cardiovascular diseases. The method employs a Viola-Jones detector that has been specially adapted for efficient detection of transverse sections of the carotid artery. This algorithm is trained on a set of labeled images using the AdaBoost algorithm, Haar-like features and the Matthews coefficient. The training algorithm of the artery detector was modified using evolutionary algorithms. The method for training a cascade of classifiers achieves on a small number of positive and negative training data samples (about 500 images) a high success rate in a computational time that allows implementation of the detector in real time. Testing was performed on images of different patients for whom different ultrasonic instruments were used under different conditions (settings) so that the algorithm developed is applicable in general radiologic practice.