Improved Segmentation for Intravascular Ultrasound (IVUS) Modality

  • Authors

    • Suhaili Beeran Kutty
    • Rahmita Wirza O.K Rahmat
    • Sazzli Shahlan Kassim
    • Hizmawati Madzin
    • Hazlina Hamdan
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.25468
  • Catheter Detection, Coronary Artery, Intravascular Ultrasound, IVUS Segmentation, Parametric Deformable Model.
  • IVUS is the modality to investigate the internal structure of the coronary artery. Segmentation is necessary to differentiate the lumen, the media-adventitia and others feature that appears on the modality, but manual segmentation is tedious and time-consuming. To enhance the computational segmentation, this paper presents the process to segment catheter shape, inner and outer layer of the artery. The new algorithm is proposed to detect the catheter shape and percentage of the accuracy is 100%. We also provide information on detection of lumen and media-adventitia border and area using a parametric deformable model algorithm with gradient vector flow as the external force. The Percentage Area of Difference (PAD) value for the segmentation is below than one, indicate that this proposed method is highly encouraging for IVUS segmentation process. Based on the inner border detected, media-adventitia boundaries also can be detected without manual initialization points. This work is important to facilitate the process of the 3D reconstruction of the coronary artery.

     


  • References

    1. [1] H. Sofian, J. C. M. Than, N. Mohd Noor, and H. Dao, “Segmentation and detection of media adventitia coronary artery boundary in medical imaging intravascular ultrasound using Otsu thresholding,†in BioSignal Analysis, Processing and Systems (ICBAPS), 2015 International Conference on, 2015, pp. 72–76.

      [2] F. S. Zakeri, S. K. Setarehdan, and S. Norouzi, “Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model,†Comput. Biol. Med., no. March, pp. 1–12, 2017.

      [3] J. Yan and Y. Cui, “A novel approach for segmentation of intravascular ultrasound images,†in Bioelectronics and Bioinformatics (ISBB), 2015 International Symposium on, 2015, pp. 51–54.

      [4] M. B. Tayel, M. A. Massoud, and Y. Farouk, “A modified segmentation method for determination of IV vessel boundaries,†Alexandria Eng. J., vol. 56, no. 4, pp. 449–457, 2017.

      [5] S. Su, Z. Gao, H. Zhang, Q. Lin, W. K. Hau, and S. Li, “Detection of lumen and media-adventitia borders in ivus images using sparse auto-encoder neural network,†in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, pp. 1120–1124.

      [6] C. H. Chen and A. G. Gangidi, “Automatic Segmentation of Intravascular Ultrasound Images based on Temporal Texture Analysis,†Comput. Cardiol. (2010)., vol. 41, pp. 957–960, 2014.

      [7] R. Sanz-Requena, D. Moratal, D. R. García-Sánchez, V. Bodí, J. J. Rieta, and J. M. Sanchis, “Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies,†Comput. Med. Imaging Graph., vol. 31, pp. 71–80, 2007.

      [8] D. D. Patil and S. G. Deore, “Medical Image Segmentation : A Review,†Int. J. Comput. Sci. Mob. Comput., vol. 2, no. 1, pp. 22–27, 2013.

      [9] A. Katouzian, E. D. Angelini, S. G. Carlier, J. S. Suri, N. Navab, and A. F. Laine, “A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images,†IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 5, pp. 823–834, 2012.

      [10] M. A. Hamdi, K. S. Ettabaa, M. L. Harabi, and L. Alvarez, “Real time IVUS segmentation and plaque characterization by combining morphological snakes and contourlet transform,†in Recent Advances in Biomedical & Chemical Engineering and Materials Science, 2014, pp. 160–166.

      [11] M. Papadogiorgaki, V. Mezaris, Y. S. Chatzizisis, G. D. Giannoglou, and I. Kompatsiaris, “Image Analysis Techniques for Automated IVUS Contour Detection,†Ultrasound Med. Biol., vol. 34, no. 9, pp. 1482–1498, 2008.

      [12] A. Taki et al., “Automatic segmentation of calcified plaques and vessel borders in IVUS images,†Int. J. Comput. Assist. Radiol. Surg., vol. 3, pp. 347–354, 2008.

      [13] Q. Zhang, Y. Wang, W. Wang, M. Jianying, J. Qian, and J. Ge, “Contour extraction from IVUS images based on GVF snakes and wavelet transform,†in 2007 IEEE/ICME International Conference on Complex Medical Engineering, 2007, pp. 536–541.

      [14] G. D. Giannoglou et al., “A novel active contour model for fully automated segmentation of intravascular ultrasound images: In vivo validation in human coronary arteries,†Comput. Biol. Med., vol. 37, no. 9, pp. 1292–1302, 2007.

      [15] M.-H. R. Cardinal, J. Meunier, G. Soulez, R. L. Maurice, E. Therasse, and G. Cloutier, “Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions.,†IEEE Trans. Med. Imaging, vol. 25, no. 5, pp. 590–601, 2006.

      [16] E. Brunenberg, O. Pujol, B. ter Haar Romeny, and P. Radeva, “Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake.,†Med. Image Comput. Comput. Assist. Interv., vol. 9, no. Pt 2, pp. 9–16, 2006.

      [17] G. Unal, S. Bucher, S. Carlier, G. Slabaugh, T. Fang, and K. Tanaka, “Shape-driven Segmentation of Intravascular Ultrasound Images,†in Proc MICCAI Workshop in Computer Vision for Intravascular and Intracardiac Imaging, 2006, pp. 50–57.

      [18] M. E. Plissiti, D. I. Fotiadis, L. K. Michalis, and G. E. Bozios, “An Automated Method for Lumen and Media–Adventitia Border Detection in a Sequence of IVUS Frames,†IEEE Trans. Inf. Technol. Biomed., vol. 8, no. 2, pp. 131–141, Jun. 2004.

      [19] M. Eslamizadeh, G. Attarodi, N. J. Dabanloo, and J. F. Sedehi, “The Segmentation of Lumen Boundaries at Intravascular Ultrasound Images Using Fuzzy Approach,†in 2017 Computing in Cardiology (CinC), 2017, vol. 44, pp. 1–4.

      [20] Debarghya China, M. K. Nag, K. M. Mandana, A. K. Sadhu, P. Mitra, and C. Chakraborty, “Automated in vivo delineation of lumen wall using intravascular ultrasound imaging,†in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, pp. 4125–4128.

      [21] C. H. Chen and A. G. Gangidi, “Automatic Segmentation of Intravascular Ultrasound Images based on Temporal Texture Analysis,†in Computing in Cardiology, 2014, pp. 957–960.

      [22] S. Sun, M. Sonka, and R. R. Beichel, “Graph-based ivus segmentation with efficient computer-aided refinement,†IEEE Trans Med Imaging, vol. 32, no. 8, pp. 997–1003, 2013.

      [23] H. Lazrag and M. S. Naceur, “Combination of the level-set methods with the contourlet transform for the segmentation of the IVUS images,†Int. J. Biomed. Imaging, vol. 2012, 2012.

      [24] F. Ciompi, O. Pujol, C. Gatta, X. Carrillo, J. Mauri, and P. Radeva, “A Holistic approach for the detection of media-adventitia border in IVUS,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6893 LNCS, no. PART 3, pp. 411–419, 2011.

      [25] X. Zhu, P. Zhang, J. Shao, Y. Cheng, Y. Zhang, and J. Bai, “A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation,†Ultrasonics, vol. 51, no. 2, pp. 181–189, 2011.

      [26] S. Balocco et al., “Standardized evaluation methodology and reference database for evaluating IVUS image segmentation,†Comput. Med. Imaging Graph., vol. 38, no. 2, pp. 70–90, 2014.

      [27] M. Zheng, W. Yubin, W. Yousheng, S. Xiaodi, and W. Yali, “Detection of the lumen and media-adventitia borders in IVUS imaging,†in International Conference on Signal Processing Proceedings, ICSP, 2008, vol. 1, no. 1, pp. 1059–1062.

      [28] D. A. Kumar, “A new Method on Brain MRI Image Preprocessing for Tumor Detection,†Int. J. Sci. Res. Sci. Eng. Technol., vol. 1, no. 1, pp. 40–44, 2015.

      [29] N. Shameena and R. Jabbar, “A Study of Preprocessing and Segmentation Techniques on Cardiac Medical Images,†Int. J. Eng. Res. Technol., vol. 3, no. 4, pp. 336–341, 2014.

      [30] V. Rajamani, S. Jaiganesh, and P. Babu, “A review of various global contrast enhancement techniques for still images using histogram modification framework,†Int. J. Eng. Trends Technol., vol. 4, no. 4, pp. 1045–1048, 2013.

      [31] A. Mölder, S. Czanner, N. Costen, and G. Hartshorne, “Automatic detection of embryo location in medical imaging using trigonometric rotation for noise reduction,†in Proceedings - International Conference on Pattern Recognition, 2014, pp. 3239–3244.

      [32] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, Second., vol. Second Ed. Gatesmark,LLC,USA: Pearson Education,Inc, 2011.

      [33] C. Xu, “Deformable Models with Application to Human Cerebral Cortex Reconstruction from Magnetic Resonance Images,†1999.

      [34] C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,†IEEE Trans. Image Process., vol. 7, no. 3, pp. 359–369, 1998.

      [35] Xu, Chenyang and J. L. Prince, “Gradient vector flow: a new external force for snakes,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2, no. 1, pp. 66–71, 1997.

      [36] C. Xu, D. Pham, and J. Prince, “Chapter 3: Image segmentation using deformable models,†in Handbook of Medical Imaging, 2000, pp. 129–174.

      [37] A. Farag, Ed., Deformable Models - Theory and Biomaterial Applications, 1st ed. Springer-Verlag New York, 2007.

      [38] M. Kass, A. Witkin, and D. Terzopoulos, “Active contour models,†Int. J. Comput. Vis., vol. 1, no. 4, pp. 133–144, 1987.

  • Downloads

  • How to Cite

    Beeran Kutty, S., Wirza O.K Rahmat, R., Shahlan Kassim, S., Madzin, H., & Hamdan, H. (2018). Improved Segmentation for Intravascular Ultrasound (IVUS) Modality. International Journal of Engineering & Technology, 7(4.31), 479-486. https://doi.org/10.14419/ijet.v7i4.31.25468