Journal of Digital Imaging

, Volume 24, Issue 4, pp 598–608 | Cite as

An Image-Based Comprehensive Approach for Automatic Segmentation of Left Ventricle from Cardiac Short Axis Cine MR Images

  • Su Huang
  • Jimin Liu
  • Looi Chow Lee
  • Sudhakar K Venkatesh
  • Lynette Li San Teo
  • Christopher Au
  • Wieslaw L. Nowinski
Article

Abstract

Segmentation of the left ventricle is important in the assessment of cardiac functional parameters. Manual segmentation of cardiac cine MR images for acquiring these parameters is time-consuming. Accuracy and automation are the two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach to segment the left ventricle from short axis cine cardiac MR images automatically. Our method incorporates a number of image processing and analysis techniques including thresholding, edge detection, mathematical morphology, and image filtering to build an efficient process flow. This process flow makes use of various features in cardiac MR images to achieve high accurate segmentation results. Our method was tested on 45 clinical short axis cine cardiac images and the results are compared with manual delineated ground truth (average perpendicular distance of contours near 2 mm and mean myocardium mass overlapping over 90%). This approach provides cardiac radiologists a practical method for an accurate segmentation of the left ventricle.

Key words

Image segmentation cardiac imaging image analysis left ventricle 

References

  1. 1.
    Selvanayagam JB, Robson MD, Francis JM, Neubauer S: Cardiovascular Magnetic Resonance: Basic Principles, Methods and Techniques. In: Dilsizian V, Pohost GM Eds. Cardiac CT, PET and MRI. Blackwell, Oxford, 2007, pp 28–68Google Scholar
  2. 2.
    Lu Y, Radau P, Connelly K, Dick A, Wright GA: Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method: LNCS 5528:339–347, 2008Google Scholar
  3. 3.
    Cocosco CA, Niessen WJ, Netsch T, Vonken EPA, Lund G, Stork A, Viergever MA: Automatic image-driven segmentation of the ventricles in cardiac cine MRI. J Magn Reson Imaging 28:366–374, 2008PubMedCrossRefGoogle Scholar
  4. 4.
    Lorenzo-Valdes M, Sanchez-Ortiz GI, Elkington AG, Mohiaddin RH, Rueckert D: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med Image Anal 8:255–265, 2004PubMedCrossRefGoogle Scholar
  5. 5.
    Pednekar A, Kurkure U, Muthupillai R, Flamm S, Kakadiaris IA: Automated left ventricular segmentation in cardiac MRI. IEEE Transactions on Biomedical Engineering 53:1425–1428, 2006PubMedCrossRefGoogle Scholar
  6. 6.
    Uzümcü M, van der Geest RJ, Swingen C, Reiber JH, Lelieveldt BP: Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming. Invest Radiol 41:52–62, 2006PubMedCrossRefGoogle Scholar
  7. 7.
    Rezaee MR, van der Zwet PMJ, Lelieveldt BPE, van der Geest RJ, Reiber JHC: A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE Transactions on Image Processing 9:1238–1248, 2000PubMedCrossRefGoogle Scholar
  8. 8.
    Kaus MR, Berg Jv, Weese J, Niessen W, Pekar V: Automated segmentation of the left ventricle in cardiac MRI. Med Image Anal 8:245–254, 2004PubMedCrossRefGoogle Scholar
  9. 9.
    Mitchell SC, Lelieveldt BPF, van der Geest RJ, Bosch HG, Reiver JHC, Sonka M: Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 20:415–423, 2001PubMedCrossRefGoogle Scholar
  10. 10.
    Paragios N: A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans Med Imaging 22:773–776, 2003PubMedCrossRefGoogle Scholar
  11. 11.
    Fradkin M, Ciofolo C, Mory B, Hautvast G, Breeuwer M: Comprehensive segmentation of cine cardiac MR images. Med Image Comput Comput Assist Interv 11:178–185, 2008PubMedGoogle Scholar
  12. 12.
    Lynch M, Ghita O, Whelan PF: Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model. IEEE Trans Med Imaging 27:195–203, 2008PubMedCrossRefGoogle Scholar
  13. 13.
    Boykov Y, Jolly M-P: Interactive Organ Segmentation Using Graph Cuts. Proceedings of MICCAI, 2000, pp 276–286Google Scholar
  14. 14.
    Lin X, Cowan B, Young A: Model-Based Graph Cut Method for Segmentation of the Left Ventricle. 27th Annual International Conference Proceedings of the Engineering in Medicine and Biology Society, IEEE-EMBS, 2005, pp 3059–3062Google Scholar
  15. 15.
    Frangi AF, Niessen WJ, Viergever MA: Three-dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans Med Imaging 20:2–5, 2001PubMedCrossRefGoogle Scholar
  16. 16.
    Otsu N: A threshold selection method from gray-level histograms. IEEE Trans Systems Man Cybernet 9:62–66, 1979CrossRefGoogle Scholar
  17. 17.
    Coope ID: Circle fitting by linear and nonlinear least squares. J Optim Theory Appl 76:381–388, 1993CrossRefGoogle Scholar
  18. 18.
    Liao PS, Chen TS, Chung PC: A fast algorithm for multilevel thresholding. J Inf Sci Eng 17:713–727, 2001Google Scholar
  19. 19.
    Canny J: A computational approach to edge detection. Pattern analysis and machine intelligence. IEEE Transactions on PAMI 8:679–698, 1986CrossRefGoogle Scholar
  20. 20.
    Lorensen WE, Cline HE: Marching cubes: a high resolution 3D surface construction algorithm. Comput Graph 21:163–169, 1987CrossRefGoogle Scholar
  21. 21.
    Kass M, Witkin A, Terzopoulos D: Snakes: active contour models. Int J Comput Vision 1:321–331, 1988CrossRefGoogle Scholar
  22. 22.
    Liu J, Huang S, Nowinski WL: A hybrid approach for segmentation of anatomic structures in medical images. International Journal of Computer Assisted Radiology and Surgery 3:213–219, 2008CrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • Su Huang
    • 1
  • Jimin Liu
    • 1
  • Looi Chow Lee
    • 1
  • Sudhakar K Venkatesh
    • 2
  • Lynette Li San Teo
    • 2
  • Christopher Au
    • 2
  • Wieslaw L. Nowinski
    • 1
  1. 1.Biomedical Imaging Lab, Singapore Bio-imaging ConsortiumAgency for Science, Technology and Research (A*STAR)SingaporeSingapore
  2. 2.Department of Diagnostic Radiology, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore

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