We propose a fully automatic method for detecting the carotid artery from volumetric ultrasound images as a preprocessing stage for building three-dimensional images of the structure of the carotid artery. The proposed detector utilizes support vector machine classifiers to discriminate between carotid artery images and non-carotid artery images using two kinds of LBP-based features. The detector switches between these features depending on the anatomical position along the carotid artery. We evaluate our proposed method using actual clinical cases. Accuracies of detection are 100%, 87.5% and 68.8% for the common carotid artery, internal carotid artery, and external carotid artery sections, respectively.

In this paper, we propose a fully automatic method for extracting carotid artery contours from ultrasound images based on an active contour approach. Several contour extraction techniques have been proposed to measure carotid artery walls for early detection of atherosclerotic disease. However, the majority of these techniques require a certain degree of user interaction that demands time and effort. Our proposal automatically detects the position of the carotid artery by identifying blood flow information related to the carotid artery, and an active contour model is employed that uses initial contours placed in the detected position. Our method also applies a global energy minimization scheme to the active contour model. Experiments on clinical cases show that the proposed method automatically extracts the carotid artery contours at an accuracy close to that achieved by manual extraction.

The purpose of this study is to develop an automated scheme of carotid artery calcification (CAC) detection on dental panoramic radiographs (DPRs). The CAC is one of the indices for predicting the risk of arteriosclerosis. First, regions of interest (ROIs) that include carotid arteries are determined on the basis of inflection points of the mandibular contour. Initial CAC candidates are detected by using a grayscale top-hat filter and a simple grayscale thresholding technique. Finally, a rule-based approach and a support vector machine to reduce the number of false positive (FP) findings are applied using features such as area, location, and circularity. A hundred DPRs were used to evaluate the proposed scheme. The sensitivity for the detection of CACs was 90% with 4.3 FPs (80% with 1.9 FPs) per image. Experiments show that our computer-aided detection scheme may be useful to detect CACs.

A continuous-wave ultrasonic Doppler system using wide field ultrasound transducers was applied to telemeter blood velocity from the carotid artery of exercising subjects. Velocity spectrogram was obtained by Hanning windowed fast Fourier transformation of the telemetered data. Distortion caused by a high-pass filter and transducers in the telemetry system was discussed in the paper. As the maximum Reynolds number in our experiment was 1478 which is smaller than the critical level of 2000, the blood flow should be laminar. Spatial velocity profiles were then reconstructed from the velocity spectrogram. In this paper, we defined a converging index Q of the velocity spectrum to measure the bluntness of the spatial velocity distribution across the blood vessel. Greater Q, the blunter the velocity profile will be. Simulation results for spatial velocity distributions of theoretical parabolic flow and Gaussian-distribution spectra with varied Q value showed that the cut-off effect by a high-pass filter of cut-off frequency fc=200Hz in our system could be ignored when the axial velocity is larger than 0.30 m/s and Q is greater than 2.0. Our experimental results, in contrast to those obtained from phantom systems by us and by Hein and O'Brien, indicate that the distribution of blood velocity is much blunter than previously thought. The Q index exceeded 10 during systole, whereas it was 0.5 in parabolic flow. The peak of Q index lagged behind that of axial blood velocity by approximately 0.02s. The phase delay of the Q index curve might be due to the time needed for the red blood cells to form the non-homogeneous distribution.