Semi-Automated Classification of the Physiological Condition of the Carotid Artery in 2D Ultrasound Image Sequences

-A novel automated method for the classification of the physiological condition of the carotid artery in 2D ultrasound image sequences is introduced. Unsupervised clustering was applied for the segmentation process in which both spatial and temporal information was utilized. Radial distension is then measured in the inner surface of the vessel wall, and this characteristic signal is extracted to reveal the detailed radial motion of the variable inner part of the vessel wall that is in contact with flowing blood. Characteristic differences in this time signal were noticed among healthy young, healthy elderly and pathological elderly cases. The discrete Fourier transform of the radial distension signal is then computed, and the area subtended by the transform is calculated and utilized as a diagnostic feature. The method was tested successfully and could differentiate among the categories of patients mentioned above. Therefore, this computer-aided method would significantly simplify the task of medical specialists in detecting any defects in the carotid artery and thereby in detecting early cardiovascular symptoms. The significance of the proposed method is that it is intuitive, semi-automatic, reproducible, and significantly reduces the reliance upon subjective measures. Key-Words: -Unsupervised clustering, ultrasound image segmentation, k-means algorithm, discrete Fourier transform DFT, carotid artery, radial distension.