Skip to main content
Springer Nature Link
Log in

Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The intima media thickness (IMT) of common carotid artery is a reliable measure of cardiovascular diseases. The quantification of IMT is the biomarker for clinical diagnosis of the risk of stroke. For robust measurement of IMT, the ultrasound carotid images must be free of speckle noise. To reduce the effect of speckle noise in the carotid ultrasound image, we propose to use Bayesian least square estimation filter. In addition, the enhancement step based on total variation-\(L_{1}\)(TV-\(L_{1}\)) norm is performed to improve the robustness. Further more, we present a fully automated region of interest and segmentation of intima media complex based on support vector machine. The quantitative evaluation is carried out on 49 carotid ultrasound images. The proposed algorithm is compared with gradient-based methods like model based, dynamic programming, snake algorithm, and classifier-based segmentation using a neural network algorithm. The performance of the experimental result shows that the proposed method is robust in quantifying the IMT in carotid ultrasound images.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. van der Meer, I.M.; Bots, M.L.; Hofman, A.; del Sol, A.I.; van der Kuip, D.A.; Witteman, J.C.: Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction. Circulation 109(9), 1089–1094 (2004)

    Article  Google Scholar 

  2. Go, A.S.; Mozaffarian, D.; Roger, V.L.; Benjamin, E.J.; Berry, J.D.; Borden, W.B.; Bravata, D.M.; Dai, S.; Ford, E.S.; Fox, C.S.; et al.: Heart disease and stroke statistics-2013 update. Circulation 127(1) (2013)

  3. University, C.: E-health laboratory cs department. (www.medinfo.cs.ucy.ac.cy/index.php/downloads/datasets) (2007)

  4. Simon, A.; Gariepy, J.; Chironi, G.; Megnien, J.L.; Levenson, J.: Intima-media thickness: a new tool for diagnosis and treatment of cardiovascular risk. J. Hypertens. 20(2), 159–169 (2002)

    Article  Google Scholar 

  5. Lamont, D.; Parker, L.; White, M.; Unwin, N.; Bennett, S.M.; Cohen, M.; Richardson, D.; Dickinson, H.O.; Adamson, A.; Alberti, K.; et al.: Risk of cardiovascular disease measured by carotid intima-media thickness at age 49–51: lifecourse study. Bmj 320(7230), 273–278 (2000)

    Article  Google Scholar 

  6. Aja-Fernández, S.; Alberola-López, C.: On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Image Process. 15(9), 2694–2701 (2006)

    Article  Google Scholar 

  7. Lee, J.S.: Speckle suppression and analysis for synthetic aperture radar images. Opt. Eng. 25(5), 170–179 (1986)

    Article  Google Scholar 

  8. Frost, V.S.; Stiles, J.A.; Shanmugan, K.S.; Holtzman, J.C.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 2, 157–166 (1982)

    Article  Google Scholar 

  9. Buades, A.; Coll, B.; Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005. vol. 2, pp. 60–65. IEEE (2005)

  10. Tian, J.; Chen, L.: Image despeckling using a non-parametric statistical model of wavelet coefficients. Biomed. Signal Process. Control 6(4), 432–437 (2011)

    Article  Google Scholar 

  11. Mitra, P.; Chakraborty, C.; Mandana, K.: Wavelet based non local means filter for despeckling of intravascular ultrasound image. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1361–1365. IEEE (2015)

  12. Parrilli, S.; Poderico, M.; Angelino, C.V.; Verdoliva, L.: A nonlocal sar image denoising algorithm based on llmmse wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50(2), 606–616 (2012)

    Article  Google Scholar 

  13. Nagaraj, Y.; Asha, C.; Narasimhadhan, A.: Assessment of speckle denoising in ultrasound carotid images using least square bayesian estimation approach. In: Region 10 Conference (TENCON), 2016 IEEE, pp. 1001–1004. IEEE (2016)

  14. Touboul, P.J.; Prati, P.; Scarabin, P.Y.; Adrai, V.; Thibout, E.; Ducimetière, P.: Use of monitoring software to improve the measurement of carotid wall thickness by b-mode imaging. J. Hypertens. 10, S37–S42 (1992)

    Google Scholar 

  15. Pignoli, P.; Longo, T.: Evaluation of atherosclerosis with b-mode ultrasound imaging. J. Nucl. Med. Allied Sci. 32(3), 166–173 (1987)

    Google Scholar 

  16. Liguori, C.; Paolillo, A.; Pietrosanto, A.: An automatic measurement system for the evaluation of carotid intima-media thickness. IEEE Trans. Instrum. Meas. 50(6), 1684–1691 (2001)

    Article  Google Scholar 

  17. Gutierrez, M.; Pilon, P.; Lage, S.; Kopel, L.; Carvalho, R.; Furuie, S.: Automatic measurement of carotid diameter and wall thickness in ultrasound images. In: Computers in Cardiology, 2002, pp. 359–362. IEEE (2002)

  18. Golemati, S.; Stoitsis, J.; Balkizas, T.; Nikita, K.: Comparison of b-mode, m-mode and hough transform methods for measurement of arterial diastolic and systolic diameters. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005., pp. 1758–1761. IEEE (2006)

  19. Golemati, S.; Stoitsis, J.; Sifakis, E.G.; Balkizas, T.; Nikita, K.S.: Using the hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. Ultrasound Med. Biol. 33(12), 1918–1932 (2007)

    Article  Google Scholar 

  20. Stein, J.H.; Korcarz, C.E.; Mays, M.E.; Douglas, P.S.; Palta, M.; Zhang, H.; LeCaire, T.; Paine, D.; Gustafson, D.; Fan, L.: A semiautomated ultrasound border detection program that facilitates clinical measurement of ultrasound carotid intima-media thickness. J. Am. Soc. Echocardiogr. 18(3), 244–251 (2005)

    Article  Google Scholar 

  21. Mao, F.; Gill, J.; Downey, D.; Fenster, A.: Segmentation of carotid artery in ultrasound images: method development and evaluation technique. Med. Phys. 27(8), 1961–1970 (2000)

    Article  Google Scholar 

  22. Li, Q.; Zhang, W.; Guan, X.; Bai, Y.; Jia, J.: An improved approach for accurate and efficient measurement of common carotid artery intima-media thickness in ultrasound images. BioMed Res. Int. 2014 (2014)

  23. Loizou, C.P.; Pattichis, C.S.; Pantziaris, M.; Tyllis, T.; Nicolaides, A.: Snakes based segmentation of the common carotid artery intima media. Med. Biol. Eng. Comput. 45(1), 35–49 (2007)

    Article  Google Scholar 

  24. Loizou, C.P.; Pattichis, C.S.; Nicolaides, A.N.; Pantziaris, M.: Manual and automated media and intima thickness measurements of the common carotid artery. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56(5) (2009)

  25. Loizou, C.P.; Kasparis, T.; Lazarou, T.; Pattichis, C.S.; Pantziaris, M.: Manual and automated intima-media thickness and diameter measurements of the common carotid artery in patients with renal failure disease. Comput. Biol. Med. 53, 220–229 (2014)

    Article  Google Scholar 

  26. Loizou, C.P.; Nicolaides, A.; Kyriacou, E.; Georghiou, N.; Griffin, M.; Pattichis, C.S.: A comparison of ultrasound intima-media thickness measurements of the left and right common carotid artery. IEEE J. Transl. Eng. Health Med. 3, 1–10 (2015)

    Article  Google Scholar 

  27. Delsanto, S.; Molinari, F.; Giustetto, P.; Liboni, W.; Badalamenti, S.; Suri, J.S.: Characterization of a completely user-independent algorithm for carotid artery segmentation in 2-d ultrasound images. IEEE Trans. Instrum. Meas. 56(4), 1265–1274 (2007)

    Article  Google Scholar 

  28. Destrempes, F.; Meunier, J.; Giroux, M.F.; Soulez, G.; Cloutier, G.: Segmentation in ultrasonic b-mode images of healthy carotid arteries using mixtures of nakagami distributions and stochastic optimization. IEEE Trans. Med. Imaging 28(2), 215–229 (2009)

    Article  Google Scholar 

  29. Ilea, D.E.; Whelan, P.F.; Brown, C.; Stanton, A.: An automatic 2d cad algorithm for the segmentation of the imt in ultrasound carotid artery images. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 515–519. IEEE (2009)

  30. Ilea, D.E.; Duffy, C.; Kavanagh, L.; Stanton, A.; Whelan, P.F.: Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 60(1), 158–177 (2013)

    Article  Google Scholar 

  31. Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier, New York City (2014)

    MATH  Google Scholar 

  32. Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013)

    Article  Google Scholar 

  33. Eberhart, R.; Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. MHS’95., pp. 39–43. IEEE (1995)

  34. Bayraktar, Z.; Komurcu, M.; Bossard, J.A.; Werner, D.H.: The wind driven optimization technique and its application in electromagnetics. IEEE Trans. Antennas Propag. 61(5), 2745–2757 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  35. Li, H.; Zhang, S.; Ma, R.; Chen, H.; Xi, S.; Zhang, J.; Fang, J.: Ultrasound intima-media thickness measurement of the carotid artery using ant colony optimization combined with a curvelet-based orientation-selective filter. Med. Phys. 43(4), 1795–1807 (2016)

    Article  Google Scholar 

  36. Menchón-Lara, R.M.; Bastida-Jumilla, M.C.; Morales-Sánchez, J.; Sancho-Gómez, J.L.: Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks. Med. Biol. Eng. Comput. 52(2), 169–181 (2014)

    Article  Google Scholar 

  37. Araki, T.; Jain, P.K.; Suri, H.S.; Londhe, N.D.; Ikeda, N.; El-Baz, A.; Shrivastava, V.K.; Saba, L.; Nicolaides, A.; Shafique, S.: Stroke risk stratification and its validation using ultrasonic echolucent carotid wall plaque morphology: a machine learning paradigm. Comput. Biol. Med. 80, 77–96 (2017)

    Article  Google Scholar 

  38. Nagaraj, Y.; Asha, C.S.; H.S.T.A.; Narasimhadhan, A.V.: Carotid wall segmentation in longitudinal ultrasound images using structured random forest (2018). https://doi.org/10.1016/j.compeleceng.2018.02.010

  39. Wong, A.; Mishra, A.; Bizheva, K.; Clausi, D.A.: General bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery. Opt. Express 18(8), 8338–8352 (2010)

    Article  Google Scholar 

  40. Zhang, Q.; Li, C.; Han, H.; Yang, L.; Wang, Y.; Wang, W.: Computer-aided quantification of contrast agent spatial distribution within atherosclerotic plaque in contrast-enhanced ultrasound image sequences. Biomed. Signal Process. Control 13, 50–61 (2014)

    Article  Google Scholar 

  41. Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997)

    Article  Google Scholar 

  42. Ghita, O.; Ilea, D.E.; Whelan, P.F.: Texture enhanced histogram equalization using \(tv-l1\) image decomposition. IEEE Trans. Image Process. 22(8), 3133–3144 (2013)

    Article  Google Scholar 

  43. Nagaraj, Y.; Pardhu, M.; J.R.K.K.; Narasimhadhan, A.V.: Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique (2017). https://doi.org/10.1016/j.bspc.2017.08.009

  44. Meyer, F.: Topographic distance and watershed lines. Signal Process. 38(1), 113–125 (1994)

    Article  MATH  Google Scholar 

  45. Gonzalez, R.C.E.; Woods, S.L.; Gonzalez, R.E.R.E.R.C.; Woods, R.E.; Eddins, S.L.: Digital image processing using MATLAB. 04; TA1637, G6. (2004)

  46. Chang, C.C.; Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)

    Google Scholar 

  47. Vert, J.P.; Tsuda, K.; Schölkopf, B.: A primer on kernel methods. Kernel Methods Comput. Biol. pp. 35–70 (2004)

  48. Molinari, F.; Zeng, G.; Suri, J.S.: A state of the art review on intima-media thickness (imt) measurement and wall segmentation techniques for carotid ultrasound. Comput. Methods Progr. Biomed. 100(3), 201–221 (2010)

    Article  Google Scholar 

  49. Garren, S.T.: Maximum likelihood estimation of the correlation coefficient in a bivariate normal model with missing data. Stat. Probab. Lett. 38(3), 281–288 (1998)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Department of Radiology, Father Muller Hospital, Mangalore, for validating the results.

Author information

Authors and Affiliations

  1. National Institute of Technology Karnataka, Mangalore, India

    Y Nagaraj, A Hema Sai Teja & A V Narasimhadhan

Authors
  1. Y Nagaraj
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. A Hema Sai Teja
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. A V Narasimhadhan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Y Nagaraj.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nagaraj, Y., Hema Sai Teja, A. & Narasimhadhan, A.V. Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine. Arab J Sci Eng 44, 3489–3496 (2019). https://doi.org/10.1007/s13369-018-3549-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-018-3549-8

Keywords