Semi-Automated Classification of the Physiological Condition of the Carotid Artery in 2D Ultrasound Image Sequences
2014 (English)In: WSEAS Transactions on Biology and Biomedicine, ISSN 1109-9518, ISSN E-ISSN 2224-2902, Vol. 11, 35-44 p.Article in journal (Refereed) Published
Abstract: -A novel automated method for the classification of the physiological condition of the carotid arteryin 2D ultrasound image sequences is introduced. Unsupervised clustering was applied for the segmentationprocess in which both spatial and temporal information was utilized. Radial distension is then measured in theinner surface of the vessel wall, and this characteristic signal is extracted to reveal the detailed radial motion ofthe variable inner part of the vessel wall that is in contact with flowing blood. Characteristic differences in thistime signal were noticed among healthy young, healthy elderly and pathological elderly cases. The discreteFourier transform of the radial distension signal is then computed, and the area subtended by the transform iscalculated and utilized as a diagnostic feature. The method was tested successfully and could differentiateamong the categories of patients mentioned above. Therefore, this computer-aided method would significantlysimplify the task of medical specialists in detecting any defects in the carotid artery and thereby in detectingearly cardiovascular symptoms. The significance of the proposed method is that it is intuitive, semi-automatic,reproducible, and significantly reduces the reliance upon subjective measures.
Place, publisher, year, edition, pages
2014. Vol. 11, 35-44 p.
Unsupervised clustering, ultrasound image segmentation, k-means algorithm, discrete Fourier
Medical Image Processing
IdentifiersURN: urn:nbn:se:kth:diva-143410ScopusID: 2-s2.0-84896934625OAI: oai:DiVA.org:kth-143410DiVA: diva2:706543