Carotid Artery and Jugular Vein Tracking and Differentiation Using Spatiotemporal Analysis

  • David Wang
  • Roberta Klatzky
  • Nikhil Amesur
  • George Stetten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


We have derived and evaluated parameters from ultrasound images of the neck to permit a computer to automatically characterize and differentiate between the carotid artery and jugular vein at image acquisition time during vascular interventions, given manually placed seed points. Our goal is to prevent inadvertent damage to the carotid artery when targeting the jugular vein for catheterization. We used a portable 10 MHz ultrasound system to acquire cross sectional B-mode ultrasound images of these great vessels at 10 fps. An expert user identified the vessels in the first frame by touching the vessels on the screen with his fingertip, and the computer automatically tracked the vessels and calculated a best-fit ellipse for each vessel in each subsequent frame. Vessel location and radii were further analyzed to produce parameters that proved useful for differentiating between the carotid artery and jugular vein. These parameters include relative location of the vessels, distension of the vessel walls, and consistent phase difference between the arterial and venous pulsations as determined by temporal Fourier analysis.


Carotid Artery Jugular Vein Internal Jugular Vein Manual Tracing Internal Jugular Vein Cannulation 


  1. 1.
    Davies, M.J., Cronin, K.D., Domaingue, C.M.: Pulmonary artery catheterization: an assessment of risks and benefits in 220 surgical patients. Anaesth Intensive Care 10(9) (1982)Google Scholar
  2. 2.
    Patel, C., Laboy, V., Venus, B., Mathru, M., Wier, D.: Acute complications of pulmonary artery catheter insertion in critically ill patients. Crit. Care Med. 14(3), 195–197 (1986)CrossRefGoogle Scholar
  3. 3.
    Kua, J.S., Tan, I.K., et al.: Airway obstruction following internal jugular vein cannulation. Anaesthesia 52, 776–780 (1997)CrossRefGoogle Scholar
  4. 4.
    Aoki, H., Mizobe, T., Nozuchi, S., et al.: Vertebral artery pseudoaneurysm: A rare complication of internal jugular vein catheterization. Anesth. Analg. 75, 296–298 (1992)CrossRefGoogle Scholar
  5. 5.
    Gobeil, F., Couture, P., Girard, D., Plante, R.: Carotid Artery-lnternal Jugular Fistula: Another Complication following Pulmonary Artery Catheterization via the Internal Jugular Venous Route. Anesthesiology 80, 23–232 (1994)CrossRefGoogle Scholar
  6. 6.
    Applebaum, R.M., et al.: Transesophageal echocardiographic identification of a retrograde dissection of the ascending aorta caused by inadvertent cannulation of the common carotid artery. J. Am. Soc. Echocardiogr. 10(7), 749–751 (1997)CrossRefGoogle Scholar
  7. 7.
    Zaidi, N.A., Khan, M., Naqvi, H.I., Kamal, R.S.: Cerebral infarct following central venous cannulation. Anaesthesia 53, 186–191 (1998)CrossRefGoogle Scholar
  8. 8.
    Denys, B.G., et al.: Ultrasound-assisted cannulation of the internal jugular vein: A prospective comparison to the external landmark-guided technique. Circulation 87, 1557–1562 (1993)Google Scholar
  9. 9.
    Stetten, G.D., Chib, V.: Overlaying Ultrasound Images on Direct Vision. J. Ultrasound Med. 20(3), 235–240 (2001)Google Scholar
  10. 10.
    Abolmaesumi, P., et al.: Real-Time Extraction of Carotid Artery Contours from Ultrasound Images. In: 13th IEEE Symposium on Computer-Based Medical Systems (CBMS 2000), p. 181 (2000)Google Scholar
  11. 11.
    Yeung, F., et al.: Feature-Adaptive Motion Tracking of Ultrasound Image Sequences Using A Deformable Mesh. IEEE Transactions on Medical Imaging 17(6) (December 1998)Google Scholar
  12. 12.
    Nakayama, K., Sato, S.: Ultrasonic measurement of arterial wall movement utilizing phase-tracking systems. In: Proceedings of the 10th International Congress on Medical and Biological Engineering, Dresden, Germany, p. 318 (Abstract, 1973)Google Scholar
  13. 13.
    Drukker, K., et al.: Computerized lesion detection on breast ultrasound. Medical Physics 29(7), 1438–1446 (2002)CrossRefGoogle Scholar
  14. 14.
    Ladak, H., et al.: Prostate boundary segmentation from 2D ultrasound images. Medical Physics 27(8), 1777–1788 (2000)CrossRefGoogle Scholar
  15. 15.
    Draper, K., et al.: An algorithm for automatic needle localization in ultrasound-guided breast biopsies. Medical Physics 27(8), 1971–1979 (2000)CrossRefGoogle Scholar
  16. 16.
    Wilson, L.S., Dadd, M.J., Gill, R.W.: Automatic vessel tracking and measurement for Doppler studies. Ultrasound Med. Biol. 16(7), 645–652 (1990)CrossRefGoogle Scholar
  17. 17.
    Pilu, M., Fitzgibbon, A., Fisher, R.: Ellipse-specific direct least-square fitting. In: Proceedings of the IEEE international Conference on Image Processing, vol. 3, pp. 599–602. IEEE Computer Society Press, Los Alamitos (1996)CrossRefGoogle Scholar
  18. 18.
    Troianos, C.A., et al.: Internal Jugular Vein and Carotid Artery Anatomic Relation as Determined by Ultrasonography. Anesthesiology 85, 43–48 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Wang
    • 1
    • 2
  • Roberta Klatzky
    • 1
  • Nikhil Amesur
    • 2
  • George Stetten
    • 1
    • 2
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.University of Pittsburgh Medical CenterPittsburghUSA

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