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International Conference Image Analysis and Recognition

ICIAR 2009: Image Analysis and Recognition pp 782–791Cite as

Diffusion-Based Detection of Carotid Artery Lumen from Ultrasound Images

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 5627)

Abstract

This paper presents an experimental study on the effect of using diffusion-based filters on segmenting carotid artery ultrasound images. Moreover, comparisons with other segmentation schemes, found in literature, were conducted. In this study, the segmentation process starts with the original ultrasound image as the initial image u o (the image at time t=0). Then, the image diffuses as the time t advances until a steady state is reached. At steady state, the real component of the diffused image will be a smoothed version of the input image, whereas the imaginary component will approximate a smoothed second derivative, which is used to extract the artery contours. The experimental results demonstrated the efficiency of diffusion-based filters in segmenting carotid artery ultrasound images.

Keywords

  • segmentation
  • complex diffusion
  • carotid artery lumen
  • ultrasound image
  • experimental study

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References

  1. Mao, F., Gill, J., Downey, D., Fenster, A.: Segmentation of carotid artery in ultrasound images. In: Proc. of the 22nd IEEE Annual Int. Conf. on Engineering in Medicine and Biology Society, vol. 3, pp. 1734–1737 (2000)

    Google Scholar 

  2. Abolmaesumi, P., Sirouspour, M., Salcudean, S.: Real-time extraction of carotid artery contours from ultrasound images. In: Proc. of the 13th IEEE Symposium on Computer-Based Med. Systems, June 2000, pp. 181–186 (June 2000)

    Google Scholar 

  3. Da-chuan, C., Schmidt-Trucksass, A., Kuo-Sheng, C., Sandrock, M., Qin, P., Burkhardt, H.: Automatic detection of the intimal and the adventitial layers of the common carotid artery wall in ultrasound B-mode images using snakes. In: Proc. of the Int. Conf. on Image Analysis and Processing, pp. 452–457 (September 1999)

    Google Scholar 

  4. Cohen, L.: On active contour models and balloons. Computer Vision, Graphics, and Image Processing: Image Understanding 53(2), 211–218 (1991)

    MathSciNet  MATH  Google Scholar 

  5. Hamou, A., El-Sakka, M.: A novel segmentation technique for carotid ultrasound images. In: Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing, vol. 3, pp. 521–424 (May 2004)

    Google Scholar 

  6. Canny, J.: Computational Approach To Edge Detection. IEEE Trans. on Pattern Anal. and Machine Intell. 8(6), 679–698 (1986)

    CrossRef  Google Scholar 

  7. Abdel-Dayem, A., El-Sakka, M., Fenster, A.: Watershed segmentation for carotid artery ultrasound images. In: Proc. of the IEEE Int. Conf. on Computer Systems and Applications, pp. 131–138 (January 2005)

    Google Scholar 

  8. Abdel-Dayem, A., El-Sakka, M.: Carotid Artery Contour Extraction from Ultrasound Images Using Multi-Resolution-Analysis and Watershed Segmentation Scheme. ICGST Int. J. on Graphics, Vision and Image Processing 5(9), 1–10 (2005)

    Google Scholar 

  9. Abdel-Dayem, A., El-Sakka, M.: Carotid artery ultrasound image segmentation using fuzzy region growing. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 869–878. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  10. Abdel-Dayem, A., El-Sakka, M.: Multi-Resolution Segmentation Using Fuzzy Region Growing for Carotid Artery Ultrasound Images. In: Proc. of the IEEE Int. Computer Engineering Conf., 8 Pages (December 2006)

    Google Scholar 

  11. Abdel-Dayem, A., El-Sakka, M.: Fuzzy c-means clustering for segmenting Carotid Artery Ultrasound Images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 933–948. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  12. Abdel-Dayem, A., El-Sakka, M.: Segmentation of Carotid Artery Ultrasound Images Using Graph Cuts. Int. J. for Computational Vision and Biomechanics (in press)

    Google Scholar 

  13. Xie, H., Pierce, L., Ulaby, F.: SAR speckle reduction using wavelet denoising and Markov random field modeling. IEEE Trans. Geosc. Remote Sensing 40(10), 2196–2212 (2002)

    CrossRef  Google Scholar 

  14. Argenti, F., Alparone, L.: Speckle removal from SAR images in the undecimated wavelet domain. IEEE Trans. Geosc. Remote Sensing 40(11), 2363–2374 (2002)

    CrossRef  Google Scholar 

  15. Solbo, S., Eltoft, T.: Homomorphic wavelet based-statistical despeckling of SAR images. IEEE Trans. Geosc. Remote Sensing 42(4), 711–721 (2004)

    CrossRef  Google Scholar 

  16. Solbo, S., Eltoft, T.: A Stationary Wavelet-Domain Wiener Filter for Correlated Speckle. IEEE Trans. Geosc. Remote Sensing 2, 1219–1230 (2008)

    CrossRef  Google Scholar 

  17. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. & Machine Intell. 12(7), 629–639 (1990)

    CrossRef  Google Scholar 

  18. Black, M., Sapiro, G., Marimont, D., Heeger, D.: Robust anisotropic diffusion. IEEE Trans. Image Processing 7(3), 421–432 (1998)

    CrossRef  Google Scholar 

  19. Yongjian, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Processing 11(11), 1260–1270 (2002)

    MathSciNet  CrossRef  Google Scholar 

  20. Zhang, F., Yoo, Y., Koh, L., Kim, Y.: Nonlinear Diffusion in Laplacian Pyramid Domain for Ultrasonic Speckle Reduction. IEEE Trans. Med. Imaging 26(2), 200–211 (2007)

    CrossRef  Google Scholar 

  21. Gilboa, G., Sochen, N., Zeevi, Y.: Image enhancement and Denoising by Complex Diffusion Processes. IEEE Trans. Pattern Anal.&Machine Intell. 26(8), 1020–1036 (2004)

    CrossRef  Google Scholar 

  22. Weickert, J.: Anisotropic Diffusion in Image Processing. ECMI Series, ch. 1, pp. 1–53. Teubner-Verlag (1998)

    Google Scholar 

  23. Witkin, A.P.: Scale-space filtering. In: Int. Joint Conf. on Artificial Intelligence, pp. 1019–1023 (1983)

    Google Scholar 

  24. Koenderink, J.: The structure of images. In: Biological Cybernetics, vol. 50, pp. 363–370. Springer, Heidelberg (1984)

    Google Scholar 

  25. Babaud, J., Witkin, A., Baudin, M., Duda, R.: Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans. Pattern Anal.&Machine Intell. 8(1), 26–33 (1986)

    CrossRef  MATH  Google Scholar 

  26. Romeny, B.: Geometry-Driven Diffusion in Computer Vision. Computational Imaging and Vision 1, 39–71 (1994)

    MathSciNet  MATH  Google Scholar 

  27. Osher, S., Rudin, L.: Feature-oriented image enhancement using shock filters. SIAM J. on Numerical Analysis 27(4), 919–940 (1990)

    CrossRef  MATH  Google Scholar 

  28. Saint-Marc, P., Chen, J., Medioni, G.: Adaptive smoothing: a general tool for early vision. IEEE Trans. Pattern Anal.&Machine Intell. 13(6), 514–529 (1991)

    CrossRef  Google Scholar 

  29. Catté, F., Lions, P., Morel, J., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. on Numerical Analysis 29(1), 182–193 (1992)

    MathSciNet  CrossRef  MATH  Google Scholar 

  30. Alvarez, L., Mazorra, L.: Signal and image restoration using shock filters and anisotropic diffusion. SIAM J. on Numerical Analysis 31(2), 590–605 (1994)

    MathSciNet  CrossRef  MATH  Google Scholar 

  31. Gonzalez, G., Woods, E.: Digital image processing, 3rd edn. Prentice-Hall, Englewood Cliffs (2008)

    Google Scholar 

  32. Dargherty, E., Lotufo, R.: Hands–on morphological image processing. The society of Photo-Optical Instrumentation Engineers (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Laurentian University, Sudbury, Ontario, Canada

    Amr R. Abdel-Dayem

  2. Computer Science Department, University of Western Ontario, London, Ontario, Canada

    Mahmoud R. El-Sakka

Authors
  1. Amr R. Abdel-Dayem
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  2. Mahmoud R. El-Sakka
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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Abdel-Dayem, A.R., El-Sakka, M.R. (2009). Diffusion-Based Detection of Carotid Artery Lumen from Ultrasound Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_77

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  • DOI: https://doi.org/10.1007/978-3-642-02611-9_77

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