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A Graph Based Methodology for Volumetric Left Ventricle Segmentation

  • S. P. Dakua
  • J. Abi Nahed
  • A. Al-Ansari
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 13)

Abstract

Clinician-friendly methods for cardiac image segmentation in clinical practice remain a tough challenge. Despite increased image quality including medical imaging, image segmentation continues to represent a major bottleneck in practical applications due to noise and lack of contrast. Larger standard deviation in segmentation accuracy may be expected for automatic methods when the input dataset is varied; also at some instances the radiologists find them hard in case any correction is desired. In this context, this paper presents a semi-automatic algorithm that uses anisotropic diffusion for smoothing the image and enhancing the edges followed by a new graph cut method, AnnularCut, for 3D left ventricle segmentation from some pre-selected MR slices. The main contribution, in this work, is a new formulation for preventing the cellular automation method to leak into surrounding areas of similar intensity. Another contribution is the use of level sets for segmenting the slices automatically between the preselected slices segmented by the cellular automaton. Both qualitative and quantitative evaluation performed on York and MICCAI Grand Challenge workshop database of MR images reflect the potential of the proposed method.

Keywords

Cellular automata Graph cut Segmentation MR 

References

  1. 1.
    Abouzar E, Athanasios K, Amin K, Nassir N (2013) Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI. Med Image Anal 17:236–253CrossRefGoogle Scholar
  2. 2.
    Adalsteinsson D, Sethian J (1995) A fast level set method for propagating interfaces. J Comput Phys 118:269–277CrossRefMATHMathSciNetGoogle Scholar
  3. 3.
    Andre A, Tsotsos J (2008) Efficient and generalizable statistical models of shape and appearance for analysis of CMRI. Med Image Anal 12:335–357CrossRefGoogle Scholar
  4. 4.
    Ayed I, Punithakumar K, Li S, Islam A (2009) Left ventricle segmentation via graph cut distribution. In: MICCAI Grand Challenge, Springer, pp 901–909Google Scholar
  5. 5.
    Ben Ayed I, Li S, Ross I (2009) Embedding overlap priors in variational left ventricle tracking. IEEE Trans Med Imaging 28:1902–1913Google Scholar
  6. 6.
    Boykov Y, Jolly MP (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: ICCV, vol 1, pp 105–112Google Scholar
  7. 7.
    Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277CrossRefMATHGoogle Scholar
  8. 8.
    Dakua S (2011) Performance divergence with data discrepancy: a review. Artif Intell Rev 1:1–27Google Scholar
  9. 9.
    Domany E, Kinzel W (1984) Equivalence of cellular automata to ising models and directed percolation. Phys Rev Lett 53:311–314CrossRefMathSciNetGoogle Scholar
  10. 10.
    Frangi A, Niessen W, Viergever M (2001) Three dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans Med Imaging 20(1):2–25CrossRefGoogle Scholar
  11. 11.
    Gomes J, Faugeras O (2000) Reconciling distance functions and level sets. J Vis Commun Image Represent 11:209–223CrossRefGoogle Scholar
  12. 12.
    Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1–17Google Scholar
  13. 13.
    Hae-Yeoun L, Codella N, Cham M, Weinsaft J, Wang Y (2010) Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. TBME 57:905–913Google Scholar
  14. 14.
    Heimann T et al (2009) Comparison and evaluation of methods for LV segmentation from MR datasets. IEEE Trans Med Imaging 28:1251–1265CrossRefGoogle Scholar
  15. 15.
    Herman G, Odhner D (1991) Performance evaluation of an iterative image reconstruction algorithm for positron emission tomography. IEEE Trans Med Imaging 10(3):336–346CrossRefGoogle Scholar
  16. 16.
    Ilya P, Alan S, Hamid K (2000) Image segmentation and edge enhancement with stabilized inverse diffusion equations. IEEE Trans Image Process 9(2):256–266CrossRefMATHGoogle Scholar
  17. 17.
    Jianbo S, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905Google Scholar
  18. 18.
    Kass M, Witkin A, Terzopolous D (1988) Snakes: active contour models. Int J Comput Vision 4:321–331CrossRefGoogle Scholar
  19. 19.
    Krzysztof C, Jayaram U, Falcao A, Miranda P (2012) Fuzzy connectedness image segmentation in graph cut formulation: a linear-time algorithm and a comparative analysis. Math Imaging Vis 44:375–398CrossRefMATHGoogle Scholar
  20. 20.
    Li C, Xu C, Gui C, Fox M (2005) Level set formulation without re-initialization: a new variational formulation. Proc IEEE CVPR 1:430–436Google Scholar
  21. 21.
    Lorenzo M, Sanchez G, Mohiaddin R, Rueckert D (2002) Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In: MICCAI 2002. LNCS, vol 2488. Springer, Heidelberg, pp 642–650Google Scholar
  22. 22.
    Lynch M, Ghita O, Whelan PF (2008) Segmentation of the left ventricle of the heart in 3-D\(+\)t MRI data using an optimized nonrigid temporal model. IEEE Trans Med Imaging 27:195–203CrossRefGoogle Scholar
  23. 23.
    Malladi R, Sethian J, Vemuri B (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17:158–175CrossRefGoogle Scholar
  24. 24.
    MICCAI (2009) Grand Challenge. www.smial.sri.utoronto.ca/LV_Challenge
  25. 25.
    Michael L, Ovidiu G, Paul W (2008) Segmentation of the left ventricle of the heart in 3-D\(+\)t MRI data. IEEE Trans Med Imaging 27(2):195–203Google Scholar
  26. 26.
    Mortensen EN, Barrett WA (1998) Interactive segmentation with intelligent scissors. Graphical Models Image Process 60:349–384CrossRefMATHGoogle Scholar
  27. 27.
    Nuzillard D, Lazar C (2007) Partitional clustering techniques for multi-spectral image segmentation. J Comput 2(10):1–8Google Scholar
  28. 28.
    Osher S, Sethian J (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation. J Comput Phys 79:12–49CrossRefMATHMathSciNetGoogle Scholar
  29. 29.
    Paragios N (2003) A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans Med Imaging 22(6):773–776CrossRefGoogle Scholar
  30. 30.
    Pednekar K, Muthupillai R, Flamm S, Kakadiaris I (2006) Automated left ventricular segmentation in cardiac MRI. IEEE Trans Biomed Eng 53(7):1425–1428CrossRefGoogle Scholar
  31. 31.
    Pednekar A, Kurkure U, Muthupillai R, Flamm S, Kakadiaris I (2006) Automated LV segmentation in CMRI. TBME 53:1425–1428Google Scholar
  32. 32.
    Pluempitiwiriyawej C, Moura J, Wu Y, Ho C (2005) STACS: new active contour scheme for cardiac MR image segmentation. IEEE Trans Med Imaging 24(5):593–603CrossRefGoogle Scholar
  33. 33.
    Rezaee M, Zwet P, Lelieveldt B, Geest R, Reiber J (2000) A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy Clustering. IEEE Trans Image Process 9(7):1238–1248CrossRefGoogle Scholar
  34. 34.
    Rother C, Kolmogorov V, Blake A (2004) Grabcut – interactive foreground extraction using iterated graph cuts. In: ACM SIGGRAPH, 2004Google Scholar
  35. 35.
    Song W, Jeffrey S (2003) Segmentation with ratio cut. IEEE Trans Pattern Anal Mach Intell 25(6):675–694CrossRefGoogle Scholar
  36. 36.
    Sum K, Paul C (2008) Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans Biomed Eng 55(1):358–360Google Scholar
  37. 37.
    Surendra R (1995) Contour extraction from CMRI studies using snakes. IEEE Trans Med Imaging 14(2):328–338CrossRefGoogle Scholar
  38. 38.
    Tood M (1996) The expectation maximization algorithm. IEEE Signal Process Mag 13(6):47–60CrossRefGoogle Scholar
  39. 39.
    Vanzella W, Torre V (2006) A versatile segmentation procedure. IEEE Trans Syst Man Cybern Part C 36(2):366–378CrossRefGoogle Scholar
  40. 40.
    Vezhnevets V, Konouchine V (2005) Growcut – interactive multi-label n-d image segmentation by cellular automata. In: Proceedings of Graphicon 2005, pp 150–156Google Scholar
  41. 41.
    Warfield S, Dengler J, Zaers J, Guttmann C, Gil W, Ettinger J, Hiller J, Kikinis R (1995) Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions. J Imag Guided Surg 1:326–338CrossRefGoogle Scholar
  42. 42.
    Yun Z, Papademetris X, Sinusas A, Duncan J (2010) Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model. IEEE Trans Med Imaging 29:669–687CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Qatar Science and Technology Park \ QRSCQatar FoundationDehaQatar
  2. 2.Hamad Medical CorporationQatar FoundationDehaQatar

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