Skip to main content
SpringerLink
Log in

Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The accuracy of the fibrotic plaque segmentation is vital in identifying the coronary artery stenosis. In this paper, we address an automated approach (APDE-GMM) for separating the fibrotic plaque area of intravascular optical coherence tomography (IV-OCT) images. Under this approach, an objective function consisting of a new energy functional with Rayleigh distribution and the negative log-likelihood function of Gaussian mixture model (GMM) is developed. Also, the study presents an adaptive diffusivity function where the gradient threshold can be associated to suppress the effect of speckle noise. The parameter estimation is carried out by the expectation–maximization technology. In addition, this paper derives a fourth-order partial differential equation (PDE) via Euler–Lagrange equation to obtain the optimal solutions. It has been compared to other segmentation approaches on synthetic and clinical IV-OCT images. The results demonstrate that APDE-GMM segmentates more accurately.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Zahnd, G., Hoogendoorn, A., Combaret, N., et al.: Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions. Int. J. Comput. Assist. Radiol. Surg. 12, 1923–1936 (2017)

    Article  Google Scholar 

  2. Guo, X., Tang, D., David, M., et al.: A machine learning-based method for intracoronary OCT segmentation and vulnerable coronary plaque cap thickness quantification. Int. J. Comput. Methods 15(1), 1–12 (2018)

    MATH  Google Scholar 

  3. Xu, M., Cheng, J., Wong, D.W.K.: Automatic image classification in intravascular optical coherence tomography images. In: Proceedings of IEEE Region 10 Conference (TENCON), pp. 1544–1547 (2016)

  4. Celi, S., Berti, S.: In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading. Med. Image Anal. 18(7), 1157–1168 (2014)

    Article  Google Scholar 

  5. Xu, M., Cheng, J., Li, A., et al: Fibroatheroma identification in intravascular optical coherence tomography images using deep features. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1501–1504 (2017)

  6. Ji, Z., Huang, Y., Xia, Y., et al.: A robust modified Gaussian mixture model with rough set for image segmentation. Neurocomputing 266, 550–565 (2017)

    Article  Google Scholar 

  7. Cheriyan, M.M., Prawin, A.M., Kumar, A.: Blind source separation with mixture models—a hybrid approach to MR brain classification. Magn. Reson. Imaging 54, 137–147 (2018)

    Article  Google Scholar 

  8. Banerjeel, A., Maji, P.: Constrained Students’ \(t\)-distribution based mixture model for robust image segmentation. J. Math. Imaging Vis. 60(3), 355–381 (2018)

    Article  MathSciNet  Google Scholar 

  9. Liu, Z., Song, Y., Xie, C., et al.: A new clustering method of gene expression data based on multivariate Gaussian mixture models. Signal Image Video Process. 10(2), 359–368 (2016)

    Article  Google Scholar 

  10. Ahmadvand, A., Kabiri, P.: Multispectral MRI image segmentation using Markov random field model. Signal Image Video Process. 10(2), 251–258 (2016)

    Article  Google Scholar 

  11. Barbu, T.: Nonlinear fourth-order hyperbolic PDE-based image restoration scheme. In: Proceedings of IEEE International Conference on e-Health and Bioengineering, pp. 19–21 (2015)

  12. Barbu, T.: A hybrid nonlinear fourth-order PDE-based image restoration approach. In: Proceedings of International Conference on System Theory, Control and Computing, pp. 754–756 (2016)

  13. Barbu, T.: Additive noise removal using a nonlinear hyperbolic PDE-based model. In: Proceedings of International Conference on Development and Application Systems, pp. 1–5 (2018)

  14. Heydari, M., Karami, M.R., Babakhani, A.: A new adaptive coupled diffusion PDE for MRI Rician noise. Signal Image Video Process. 10(7), 1212–1218 (2016)

    Article  Google Scholar 

  15. Soorajkumar, R., Kumar, P.K., Girish, D.R., et al: Fourth order PDE based Ultrasound despeckling using ENI classification. In: Procedings of International Conference on Signal Processing and Communication, pp. 1–5 (2016)

  16. Kumar, R., Srivastava, S., Srivastava, R.: A fourth order PDE based fuzzy c-means approach for segmentation of microscopic biopsy images in presence of poisson noise for cancer detection. Comput. Methods Programs Biomed. 146, 59–68 (2017)

    Article  Google Scholar 

  17. Schmitt, J.M.: Optical coherence tomography (OCT): a review. IEEE J. Sel. Top. Quantum Electron. 5(4), 1205–1215 (1999)

    Article  Google Scholar 

  18. You, Y.L., Kaveh, M.: Fourth-order partial differential equations for noise removal. IEEE Trans. Image Process. 9(10), 1723–1730 (2000)

    Article  MathSciNet  Google Scholar 

  19. Titterington, D.M., Smith, A.F.M., Makov, U.E.: Statistical Analysis of Finite Mixture Distributions. Wiley, Hoboken (1985)

    MATH  Google Scholar 

  20. Trivedi, M.M., Bezdek, J.C.: Low-level segmentation of aerial images with fuzzy clustering. IEEE Trans. Syst. Man Cybern. 16(4), 589–598 (1986)

    Article  Google Scholar 

  21. Sfikas, G., Nikou, C., Galatsanos, N.: Robust image segmentation with mixtures of Student’s-\(t\) distributions. In: Proceedings of IEEE International Conference on Image Processing, pp. 273–276 (2007)

  22. Nguyen, T.M., Wu, Q.M.J.: Fast and robust spatially constrained Gaussian mixture model for image segmentation. IEEE Trans. Circuits Syst. Video Technol. 23(4), 621–635 (2013)

    Article  Google Scholar 

  23. Bi, H., Tang, H., Yang, G., et al.: Accurate image segmentation using Gaussian mixture model with saliency map. Pattern Anal. Appl. 21(3), 869–878 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper. This work was supported by the National Nature Science Foundation of China under Grant 61872143.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

    Pengyu Wang, Hongqing Zhu & Xiaofeng Ling

Authors
  1. Pengyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongqing Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofeng Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongqing Zhu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, P., Zhu, H. & Ling, X. Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE. SIViP 14, 29–37 (2020). https://doi.org/10.1007/s11760-019-01520-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01520-6

Keywords

Search

Navigation