International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Left Ventricle Segmentation using Split Energy Function including Orientation Term from CTA

  • Kang, Ho Chul (School of Computer Science and Engineering SungKongHoe University)
  • Received : 2018.04.05
  • Accepted : 2018.04.20
  • Published : 2018.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an automatic left ventricle segmentation method in computed tomography angiography (CTA) using separating energy function. First, we smooth the images by applying anisotropic diffusion filter to remove noise. Secondly, the volume of interest (VOI) is detected by using k-means clustering. Thirdly, we divide the left and right heart with split energy function. Finally, we extract only left ventricle from left and right heart with optimizing cost function including orientation term.

Keywords

References

  1. I. Barandiaran, I. Macia, E. Berckmann, D. Wald, M. P. Dupillier, C. Paloc, and M. Grana, "An automatic segmentation and reconstruction of mandibular structures from CT-data," Intelligent Data Engineering and Automated Learning-IDEAL 2009, pp. 649-655, 2009. DOI: https://doi.org/10.1007/978-3-642-04394-9_79
  2. W. H. O. Director-General, "The World Health Report: Report of the Director-General," World Health Organization, 2003.
  3. O. Rousseau, and Y. Bourgault,"Heart segmentation with an iterative Chan-Vese algorithm," HAL, 2008.
  4. T. F. Chan, and L. A. Vese,"Active contours without edges," IEEE Transactions on Image Processing, Vol. 10, No. 2, pp. 266-277, 2001. DOI: https://doi.org/10.1016/j.mcm.2011.11.014
  5. O. Ecabert, J. Peters, H. Schramm, C. Lorenz, J. von Berg, M. J. Walker, M. Vembar, M. E. Olszewski, K. Subramanyan, and G. Lavi, "Automatic model-based segmentation of the heart in CT images,"IEEE Transactions on Medical Imaging, Vol. 27, No. 9, pp. 1189-1201, 2008. DOI: https://doi.org/10.1109/TMI.2008.918330
  6. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, "An efficient k-means clustering algorithm: Analysis and implementation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 881-892, 2002. DOI: https://doi.org/10.1109/TPAMI.2002.1017616
  7. Leonid I. Rudin, Stanley Osher, and Emad Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, Vol. 60, No. 1-4, pp. 259-268, 1992. DOI: https://doi.org/10.1016/0167-2789(92)90242-F
  8. R. C. Gonzalez, "Digital image processing," Pearson, 2011.
  9. P. Getreuer, "Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman," Image Processing On Line, Vol. 2, pp. 74-95, 2012 DOI: https://doi.org/10.5201/ipol.2012.g-tvd
  10. D. C. Lay, S.R. Lay and J. J. McDonald, "Linear Algebra and Its Applications," Pearson, 2015.
  11. D. Zhang, X. Jing and J. Yang, "Biometric Image Discrimination Technologies: Computational Intelligence and its Applications Series," IGP, pp.21-.40, 2006. DOI: https://doi.org/10.4018/978-1-59140-830-7
  12. M. Powell, "An efficient method for finding the minimum of a function of several variables without calculating derivatives," The Computer Journal, Vol. 7, No. 2, pp.155-162, 1964. DOI: https://doi.org/10.1093/comjnl/7.2.155