Skip to main content
SpringerLink
Log in

Automated Measurement Method of Common Carotid Artery Intima-Media Thickness in Ultrasound Image Based on Markov Random Field Models

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Common carotid artery intima-media thickness (IMT) is regarded as one of the most significant indicators for the assessment of cardiovascular diseases. This paper proposes an automated method based on Markov random field models for IMT measurement. The label field is initialized from line segments generated using the Hough transform and the iterated conditional modes method is applied to detect the final boundaries of the lumen-intima interface as well as the media-adventitia interface. A total of 80 ultrasound images from 80 corresponding patients were tested with the proposed method. The ground truth (GT) of the IMT was manually measured a total of four times and then averaged, and the automatically segmented (AS) IMT was computed using the proposed method. The mean of the absolute error ± standard deviation between AS and GT IMT values was 0.0244 ± 0.0227 mm, and the correlation coefficient between the two was 0.9810. Furthermore, the computational time was 1.7 s per image for serial computation with MATLAB on a computer with an Intel Core i5-4200M CPU running 32-bit Windows 7. Experimental results show that the proposed method is both efficient and accurate. The proposed method can be applied for real-time IMT measurement by utilizing parallel computation, which is enable by the use of Markov random field models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Global status report on non-communicable diseases, World Health Organization. (2011). Retrieved from http://www.who.int/mediacentre/factsheets/fs317/en/.

  2. Bots, M. L., Hoes, A. W., Koudstaal, P. J., Hofman, A., & Grobbee, D. E. (1997). Common carotid intima-media thickness and risk of stroke and myocardial infarction the Rotterdam Study. Circulation, 96, 1432–1437.

    Article  Google Scholar 

  3. Virtanen, J. K., Voutilainen, S., Happonen, P., Alfthan, G., Kaikkonen, J., Mursu, J., et al. (2005). Serum homocysteine, folate and risk of stroke: Kuopio ischaemic heart disease risk factor (KIHD) study. European Journal of Cardiovascular Prevention & Rehabilitation, 12, 369–375.

    Article  Google Scholar 

  4. Lorenz, M. W., Markus, H. S., Bots, M. L., Rosvall, M., & Sitzer, M. (2007). Prediction of clinical cardiovascular events with carotid intima-media thickness a systematic review and meta-analysis. Circulation, 115, 459–467.

    Article  Google Scholar 

  5. Robertson, C. M., Fowkes, F. G. R., & Price, J. F. (2012). Carotid intima–media thickness and the prediction of vascular events. Vascular Medicine, 17, 239–248.

    Article  Google Scholar 

  6. Pignoli, P., Tremoli, E., Poli, A., Oreste, P., & Paoletti, R. (1986). Intimal plus medial thickness of the arterial wall: a direct measurement with ultrasound imaging. Circulation, 74, 1399–1406.

    Article  Google Scholar 

  7. Liang, Q., Wendelhag, I., Wikstrand, J., & Gustavsson, T. (2000). A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images. IEEE Transactions on Medical Imaging, 19, 127–142.

    Article  Google Scholar 

  8. Liguori, C., Paolillo, A., & Pietrosanto, A. (2001). An automatic measurement system for the evaluation of carotid intima-media thickness. IEEE Transactions on Instrumentation and Measurement, 50, 1684–1691.

    Article  Google Scholar 

  9. Loizou, C. P., Pattichis, C. S., Pantziaris, M., Tyllis, T., & Nicolaides, A. (2007). Snakes based segmentation of the common carotid artery intima media. Medical & Biological Engineering & Computing, 45, 35–49.

    Article  Google Scholar 

  10. Molinari, F., Zeng, G., & Suri, J. S. (2010). Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 57, 1112–1124.

    Article  Google Scholar 

  11. Stoitsis, J., Golemati, S., Kendros, S. & Nikita, K. S. (2008). Automated detection of the carotid artery wall in B-mode ultrasound images using active contours initialized by the Hough transform. In Proceedings of IEEE International Conference Engineering in Medicine and Biology Society (pp. 3146–3149).

  12. Cheng, D. C., Schmidt-Trucksass, A., Cheng, K. S., & Burkhardt, H. (2002). Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images. Computer Methods and Programs in Biomedicine, 67, 27–37.

    Article  Google Scholar 

  13. Xu, X. Y., Zhou, Y., Cheng, X., Cheng, X. Y., Song, E., & Li, G. K. (2012). Ultrasound intima–media segmentation using Hough transform and dual snake model. Computerized Medical Imaging and Graphics, 36, 248–258.

    Article  Google Scholar 

  14. Cheng, D. C., & Jiang, X. (2008). Detections of arterial wall in sonographic artery images using dual dynamic programming. IEEE Transactions on Information Technology in Biomedicine, 12, 792–799.

    Article  Google Scholar 

  15. Delsanto, S., Molinari, F., Giustetto, P., Liboni, W. & Badalamenti, S. (2006). CULEX-completely user-independent layers extraction: ultrasonic carotid artery images segmentation. In Proceedings of IEEE Interenational Conference on Engineering in Medicine and Biology Society (pp. 6468–6471).

  16. Delsanto, S., Molinari, F., Giustetto, P., Liboni, W., Badalamenti, S., & Suri, J. S. (2007). Characterization of a completely user-independent algorithm for carotid artery segmentation in 2-D ultrasound images. IEEE Transactions on Instrumentation and Measurement, 56, 1265–1274.

    Article  Google Scholar 

  17. Molinari, F., Pattichis, C. S., Zeng, G., Saba, L., Acharya, U. R., Sanfilippo, R., et al. (2012). Completely automated multiresolution edge snapper—a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database. IEEE Transactions on Image Processing, 21, 1211–1222.

    Article  MathSciNet  Google Scholar 

  18. Petroudi, S.; Loizou, C; Pantziaris, M; Pattichis, M. & Pattichis, C. (2011). A fully automated method using active contours for the evaluation of the intima-media thickness in carotid US images. In Proceedings of IEEE International Conference on Engineering in Medicine and Biology Society (pp. 8053–8057).

  19. Petroudi, S., Loizou, C., Pantziaris, M., & Pattichis, C. (2012). Segmentation of the common carotid intima-media complex in ultrasound images using active contours. IEEE Transaction Biomedical Engineering, 59, 3060–3069.

    Article  Google Scholar 

  20. Menchón-Lara, R. M., Bastida-Jumilla, M. C., Morales-Sánchez, J., & Sancho-Gomez, J. (2014). Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks. Medical & Biological Engineering & Computing, 52, 169–181.

    Article  Google Scholar 

  21. Li, Q., Zhang, W., Guan, X., Bai, Y., & Jia, J. (2014). An improved approach for accurate and efficient measurement of common carotid Artery intima-media thickness in ultrasound images. BioMed Research International, doi:10.1155/2014/740328.

    Google Scholar 

  22. Destrempes, F., Meunier, J., Giroux, M. F., Soulez, G., & Cloutier, G. (2009). Segmentation in ultrasonic B-mode images of healthy carotid arteries using mixtures of Nakagami distributions and stochastic optimization. IEEE Transactions on Medical Imaging, 28, 215–229.

    Article  Google Scholar 

  23. Molinari, F., Zeng, G., & Suri, J. S. (2010). A state of the art review on intima–media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound. Computer Methods and Programs in Biomedicine, 100, 201–221.

    Article  Google Scholar 

  24. Naik, V., Gamad, R. S., & Bansod, P. P. (2013). Carotid artery segmentation in ultrasound images and measurement of intima-media thickness. BioMed Research International, doi:10.1155/2013/801962.

    Google Scholar 

  25. Li, S. Z. (1994). Markov random field models in computer vision. In Computer Vision—ECCV’94, Vol. 801 (pp. 361–370). Berlin: Springer

  26. Huang, J. & Mumford, D. (1999). Statistics of natural images and models. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, doi: 10.1109/CVPR.1999.786990.

  27. Haas, C., Ermert, H., Holt, S., Grewe, P., Machraoui, A., & Barmeyer, J. (2000). Segmentation of 3D intravascular ultrasonic images based on a random field model. Ultrasound in Medicine and Biology, 26, 297–306.

    Article  Google Scholar 

  28. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, 36, 192–236.

    MATH  MathSciNet  Google Scholar 

  29. Kim, D. W., Lee, G. H., & Kim, S. Y. (1996). Stochastic segmentation of severely degraded images using gibbs random fields. Optical Review, 3, 184–191.

    Article  Google Scholar 

  30. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactionson Pattern Analysis and Machine Intelligence, TPAMI, 6, 721–741.

    Article  MATH  Google Scholar 

  31. Marroquin, J., Mitter, S., & Poggio, T. (1987). Probabilistic solution of ill-posed problems in computational vision. Journal of the American Statistical Association, 82, 76–89.

    Article  MATH  Google Scholar 

  32. Besag, J. (1986). On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, 48, 259–302.

    MATH  MathSciNet  Google Scholar 

  33. Tomasi, C. & Manduchi, R. (1998). Bilateral filtering for gray and color images. In Proceedings of IEEE International Conference on Computer Vision (pp. 839–846).

  34. Dubes, R. C., Jain, A. K. Nadabar S. G & Chen, C. C. (1990). MRF model-based algorithms for image segmentation. In Proceedings of IEEE International Conference on Pattern Recognition, vol. 1 (pp. 808–814).

Download references

Acknowledgments

This project was supported by the National Natural Science Foundation of China (Grant 61471263). The authors sincerely thank Miss Chelsea Love for her valuable suggestions regarding this paper.

Author information

Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China

    Lu Xiao, Qiang Li, Yu Bai & Liyi Zhang

  2. Department of Automatic Control, Liaoning Jianzhu Vocational University, 24 Qingnian Road, Liaoyang, 111000, China

    Jing Tang

Authors
  1. Lu Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liyi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, L., Li, Q., Bai, Y. et al. Automated Measurement Method of Common Carotid Artery Intima-Media Thickness in Ultrasound Image Based on Markov Random Field Models. J. Med. Biol. Eng. 35, 651–660 (2015). https://doi.org/10.1007/s40846-015-0074-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-015-0074-z

Keywords

Search

Navigation