Atherosclerotic Plaque Detection Using Intravascular Ultrasound (IVUS) Images

  • A. Hari PriyaEmail author
  • R. Vanithamani
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


The atherosclerotic plaque deposition in artery is a type of cardiovascular disease and is a major factor of death. Mostly larger and high-pressure vessels such as the femoral, cerebral, renal, coronary, and carotid arteries are influenced by atherosclerosis. Hence, the characterization of plaque distribution and its liability to rupture are mandatory to judge the degree of risk and to schedule the treatment. Intravascular ultrasound (IVUS) is an ultrasound imaging modality which uses a unique catheter. The catheter will be provided with an ultrasound probe which is miniaturized and is connected to the lateral end. In this paper, segmentation of the IVUS image using fast marching method (FMM) is done and will be followed by the feature extraction. Feature extraction methods such as local binary pattern (LBP), speeded up robust feature (SURF), and histogram of oriented gradients (HOG) are used and the resulting image is classified using Euclidean distance classifier. By comparing the results of subjected feature extraction techniques, LBP method is found suitable for the detection of atherosclerotic plaque.


Intravascular ultrasound (IVUS) Atherosclerosis Fast marching method (FMM) Local binary pattern (LBP) Euclidean distance classifier 


  1. 1.
    Taki A, Roodaki A, Setarehdan SK, Avansari S, Unal G, Navab N (2013) An IVUS image-based approach for improvement of coronary plaque characterization. Comput Biol Med 43:268–280CrossRefGoogle Scholar
  2. 2.
    Taki A, Roodaki A, Pauly O, Setarehdan SK, Unal G, Navab N (2009) A new method for characterization of coronary plaque composition via IVUS IMAGES. IEEE-2009, pp 787–790Google Scholar
  3. 3.
    Zhang Q, Wang Y, Ma J, Shi J (2011) Contour detection of atherosclerotic plaques in IVUS images using ellipse template matching and particle swarm optimization. In: 33rd annual international conference of the IEEE EMBS, pp 5174–5177Google Scholar
  4. 4.
    Chen F, Ma R, Liu J, Zhu M, Liao H (2018) Lumen and media-adventitia border detection in IVUS images using texture enhanced deformable model. Comput Med Imaging Graph 66:1–13CrossRefGoogle Scholar
  5. 5.
    Zakeri FS, Setarehdan SK, Norouzi S (2017) Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model. Comput Biol Med 89:561–572CrossRefGoogle Scholar
  6. 6.
    Shi Y, Witte RS, O’Donnell M (2005) Identification of vulnerable atherosclerotic plaque using IVUS-based thermal strain imaging. IEEE Trans Ultrason Ferroelectr Freq Control 52:844–850Google Scholar
  7. 7.
    Balocco S (2014) Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph 38:70–90CrossRefGoogle Scholar
  8. 8.
    Wien TU, Ives St (2016) Comparison of the parallel fast marching method, the fast iterative method, and the parallel semi-ordered fast iterative method. In: The international conference on computational science, vol 80, pp 2271–2275Google Scholar
  9. 9.
    Krishnan S, Athavale Y (2018) Trends in biomedical signal feature extraction. Biomed Signal Process Control 43:41–63CrossRefGoogle Scholar
  10. 10.
    Anam S, Misawa H, Uchino E, Suetake N (2012) Parameter tuning by PSO for fuzzy inference-based coronary plaque extraction in IVUS image. IEEE-2012 2012:1426–1429Google Scholar
  11. 11.
    Roodaki A, Taki A, Setarehdan SK, Navab N (2008) Modified wavelet transform features for characterizing different plaque types in IVUS images. A feasibility study. IEEE-2008, pp 789–792Google Scholar
  12. 12.
    Mahale VH, Ali MH, Yannawar PL, Gaikwad AT (2017) Image inconsistency detection using local binary pattern (LBP). In: 7th international conference on advances in computing & communications, vol 115, pp 501–508CrossRefGoogle Scholar
  13. 13.
    Korkmaz SA, Binol H (2018) Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection. J Mol Struct 1156:255–263Google Scholar
  14. 14.
    Kan S-C, Cen Y-G, Cen Y, Wang Y-H, Voronin V, Mladenovic V, Zeng M (2017) SURF binarization and fast codebook construction for image retrieval. J Vis Commun Image Represent 49:104–114CrossRefGoogle Scholar
  15. 15.
    Caballero KL, Barajas J, Pujol O, Rodriguez O, Radeva P (2007) Using reconstructed IVUS images for coronary plaque classification. In: Proceedings of the 29th annual international conference of the IEEE EMBS, pp 2167–2170Google Scholar
  16. 16.
    Deza MM, Deza E, Marie M (2009) Encyclopedia of distances. Springer, pp 94Google Scholar
  17. 17.
    Li X, Li J, Jing J, Ma T, Liang S, Zhang J, Mohar D, Raney A, Mahon S, Brenner M, Patel P, Kirk Shung K, Zhou Q, Chen Z (2014) Integrated IVUS-OCT imaging for atherosclerotic plaque characterization. IEEE J Sel Top Quantum Electron 20Google Scholar
  18. 18.
    Dehnavi SM, Babu MSP, Yazchi M, Basij M (2013) Automatic soft and hard plaque detection in IVUS images: a textural approach. In: Proceedings of 2013 IEEE conference on information and communication technologies, pp 214–219Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Medical ElectronicsAvinashilingam Institute for Home Science and Higher Education for WomenCoimbatoreIndia
  2. 2.Biomedical Instrumentation EngineeringAvinashilingam Institute for Home Science and Higher Education for WomenCoimbatoreIndia

Personalised recommendations