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Automatic left and right heart segmentation using power watershed and active contour model without edge

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Abstract

Purpose

In this paper, we present an automatic method to segment a whole heart and separate left and right heart regions in cardiac computed tomography angiography (CTA) efficiently.

Methods

First, we smooth the images by applying filters to remove noise. Second, the volume of interest (VOI) is detected by using k-means clustering. In this step, the whole heart is coarsely extracted, and it is used for seed volumes in the next step. Third, we detect seed volumes using a geometric analysis based on anatomical information and separate the left and right heart with power watershed. Finally, we refine the left and right sides of the heart using active contour model without edge, which used region-based information for a more accurate segmentation.

Results

In experimental results using twenty clinical datasets, the average segmentation error was less than 5%. The average processing time was 51.66±3.35 s.

Conclusions

The proposed method extracts the left and right heart accurately, demonstrating that this approach can assist the cardiologist.

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References

  1. Schoenhagen P, Stillman AE, Halliburton SS, White RD. CT of the heart: principles, advances, clinical uses. Clev Clin J Med. 2005; 72(2):127–38.

    Article  Google Scholar 

  2. Barandiaran I, Macía I, Berckmann E, Wald D, Dupillier MP, Paloc C, Graña M. An automatic segmentation and reconstruction of mandibular structures from CT-data. Lect Notes Comput Sc. 2009; 5788:649–55.

    Article  Google Scholar 

  3. World Health Organization. The world health report: 2003: shaping the future. 20–3.

  4. Rousseau O, Bourgault Y. Heart segmentation with an iterative Chan-Vese algorithm. preprint, 20–8.

  5. Chan TF, Vese LA. Active contours without edges. IEEE T Image Process. 2001; 10(2):266–77.

    Article  MATH  Google Scholar 

  6. Van Assen HC, Danilouchkine MG, Dirksen MS, Reiber J, Lelieveldt BP. A 3-D active shape model driven by fuzzy inference: application to cardiac CT and MR. IEEE T Inf Technol B. 2008; 12(5):595–605.

    Article  Google Scholar 

  7. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE T Syst Man Cyb. 1985; SMC-15(1):116–32.

    Article  MATH  Google Scholar 

  8. Mitchell SC, Bosch JG, Lelieveldt BP, van der Geest RJ, Reiber JH, Sonka M. 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE T Med Imaging. 2002; 21(9):1167–78.

    Article  Google Scholar 

  9. Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, Weese J. Automatic model-based segmentation of the heart in CT images. IEEE T Med Imaging. 2008; 27(9):1189–201.

    Article  Google Scholar 

  10. Funka-Lea G, Boykov Y, Florin C, Jolly M-P, Moreau-Gobard R, Ramaraj R, Rinck D. Automatic heart isolation for CT coronary visualization using graph-cuts. Conf Proc Int Symp Biomed Imaging. 2006; 1:614–7.

    Google Scholar 

  11. Grady L, Sun V, Williams J. Interactive graph-based segmentation methods in cardiovascular imaging. In: Paragios N, Chen Y, Faugeras, editors. Handbook of mathematical models in computer vision. Springer; 2006. pp. 453–69.

    Chapter  Google Scholar 

  12. Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY. An efficient k-means clustering algorithm: Analysis and implementation. IEEE T Pattern Anal. 2002; 24(7):881–92.

    Article  Google Scholar 

  13. Couprie C, Grady L, Najman L, Talbot H. Power watershed: A unifying graph-based optimization framework. IEEE T Pattern Anal. 2011; 33(7):1384–99.

    Article  Google Scholar 

  14. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 1st ed. Pearson Prentice Hall; 20–4.

  15. Whitaker RT, Xue X. Variable-conductance, level-set curvature for image denoising. Conf Proc IEEE Image Proc. 2001; 3:142–5.

    Google Scholar 

  16. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE T Pattern Anal. 1990; 12(7):629–39.

    Article  Google Scholar 

  17. Sinop AK, Grady L. A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm. Conf Proc IEEE I Conf Comp Vis. 2007; 1–8.

    Google Scholar 

  18. Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pur Appl Math. 1989; 42(5):577–685.

    Article  MATH  MathSciNet  Google Scholar 

  19. Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vision. 1988; 1(4):321–31.

    Article  Google Scholar 

  20. Lorensen WE, Cline HE. Marching cubes: A high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphics. 1987; 21(4):163–9.

    Article  Google Scholar 

  21. Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE T Med Imaging. 1994; 13(4):716–24.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, Seoul, 151-742, Korea

    Ho Chul Kang & Yeong-Gil Shin

  2. School of Computer Science and Engineering, Soongsil University, Seoul, 156-743, Korea

    Bohyoung Kim

  3. Department of Radiology, Seoul National University Bundang Hospital, Gyeong gi-do, 463-707, Korea

    Jeongjin Lee

  4. Department of Systems Management Engineering, Sungkyunkwan University, Gyeong gi-do, 440-746, Korea

    Juneseuk Shin

Authors
  1. Ho Chul Kang
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  2. Bohyoung Kim
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  3. Jeongjin Lee
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  4. Juneseuk Shin
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  5. Yeong-Gil Shin
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Corresponding author

Correspondence to Jeongjin Lee.

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Kang, H.C., Kim, B., Lee, J. et al. Automatic left and right heart segmentation using power watershed and active contour model without edge. Biomed. Eng. Lett. 4, 355–361 (2014). https://doi.org/10.1007/s13534-014-0164-9

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  • DOI: https://doi.org/10.1007/s13534-014-0164-9

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