Healthc Inform Res.  2016 Oct;22(4):285-292. 10.4258/hir.2016.22.4.285.

Automatic Four-Chamber Segmentation Using Level-Set Method and Split Energy Function

• Kang HC
• Lee J
• Shin J

• KMID: 2357382
• DOI: http://doi.org/10.4258/hir.2016.22.4.285

Abstract

OBJECTIVES
In this paper, we present an automatic method to segment four chambers by extracting a whole heart, separating the left and right sides of the heart, and spliting the atrium and ventricle regions from each heart in cardiac computed tomography angiography (CTA) efficiently.
METHODS
We smooth the images by applying filters to remove noise. Next, the volume of interest 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. Then, we detect seed volumes using a geometric analysis based on anatomical information and separate the left and right heart regions with the power watershed algorithm. Finally, we refine the left and right sides of the heart using the level-set method, and extract the atrium and ventricle from the left and right heart regions using the split energy function.
RESULTS
We tested the proposed heart segmentation method using 20 clinical scan datasets which were acquired from various patients. To validate the proposed heart segmentation method, we evaluated its accuracy in segmenting four chambers based on four error evaluation metrics. The average values of differences between the manual and automatic segmentations were less than 3.3%, approximately.
CONCLUSIONS
The proposed method extracts the four chambers of the heart accurately, demonstrating that this approach can assist the cardiologist.

Keyword

Heart Segmentation; Power Watershed Anisotropic Diffusion Filter; Level Set; Split Energy Function

MeSH Terms

Angiography
Dataset
Heart
Humans
Methods*
Noise

Figure

• 

  Figure 1 Result of four-chamber segmentation: left vectricle (red area), left atrium (green area), right vectricle (blue area), and right atrium (yellow area). (A) Superimposed image in the upper axial computed tomography (CT) slice. (B) Superimposed image in the lower axial CT slice. (C) The 3D surface rendering image.

Reference

1. Schoenhagen P, Stillman AE, Halliburton SS, White RD. CT of the heart: principles, advances, clinical uses. Cleve Clin J Med. 2005; 72(2):127–138.

2. Barandiaran I, Macia I, Berckmann E, Wald D, Dupillier MP, Paloc C, et al. An automatic segmentation and reconstruction of mandibular structures from CT-data. In : Proceedings of 10th International Conference on Intelligent Data Engineering and Automated Learning; 2009 Sep 23-26; Burgos, Spain. p. 649–655.

3. World Health Organization. The world health report 2003: shaping the future. Geneva, Switzerland: World Health Organization;2003.

4. Rousseau O, Bourgault Y. Heart segmentation with an iterative Chan-Vese algorithm [Internet]. place unknown: publisher unknown;2008. cited at 2016 Oct 22. Available from: https://hal.archives-ouvertes.fr/hal-00403627v1/.

5. Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell. 1995; 17(2):158–175.

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

7. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern. 1985; 15(1):116–132.

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 Trans Med Imaging. 2002; 21(9):1167–1178.

9. Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, et al. Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging. 2008; 27(9):1189–1201.

10. Funka-Lea G, Boykov Y, Florin C, Jolly MP, Moreau-Gobard R, Ramaraj R, et al. Automatic heart isolation for CT coronary visualization using graph-cuts. In : Proceedings of 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro; 2006 Apr 6-9; Arlington, VA. p. 614–617.

11. Grady L, Sun V, Williams J. Interactive graph-based segmentation methods in cardiovascular imaging. In : Paragios N, Chen Y, Faugeras O, editors. Handbook of mathematical models in computer vision. New York (NY): Springer;2006. p. 453–469.

12. Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell. 2002; 24(7):881–892.

13. Couprie C, Grady L, Najman L, Talbot H. Power watershed: a unifying graph-based optimization framework. IEEE Trans Pattern Anal Mach Intell. 2011; 33(7):1384–1399.

14. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. Upper Saddle River (NJ): Person Prentice Hall;2004.

15. Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D. 1992; 60(1):259–268.

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

17. Sinop AK, Grady L. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In : Proceedings of 2007 IEEE 11th International Conference on Computer Vision; 2007 Oct 14-21; Rio de Janeiro, Brazil. p. 1–8.

18. Lorensen WE, Cline HE. Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comput Graph. 1987; 21(4):163–169.

19. Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging. 1994; 13(4):716–724.

20. Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process. 2001; 10(2):266–277.