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Cardiovascular Engineering

, Volume 10, Issue 3, pp 163–168 | Cite as

A Strategic Approach for Cardiac MR Left Ventricle Segmentation

  • Sarada Prasad Dakua
  • J. S. Sahambi
Technical Note

Abstract

Quantitative evaluation of cardiac function from cardiac magnetic resonance (CMR) images requires the identification of the myocardial walls. This generally requires the clinician to view the image and interactively trace the contours. Especially, detection of myocardial walls of left ventricle is a difficult task in CMR images that are obtained from subjects having serious diseases. An approach to automated outlining the left ventricular contour is proposed. In order to segment the left ventricle, in this paper, a combination of two approaches is suggested. Difference of Gaussian weighting function (DoG) is newly introduced in random walk approach for blood pool (inner contour) extraction. The myocardial wall (outer contour) is segmented out by a modified active contour method that takes blood pool boundary as the initial contour. Promising experimental results in CMR images demonstrate the potentials of our approach.

Keywords

Cardiac magnetic resonance image Gaussian weighting function Difference of Gaussian weighting function Random walk Active contour model 

Notes

Acknowledgments

We thank Alexander Andreopoulos (Andreopoulos and Tsotsos 2008) for numerous discussions concerning the data-set, and for providing the data set from Department of Diagnostic Imaging of the Hospital for Sick Children in Toronto, Canada.

References

  1. Andreopoulos A, Tsotsos J. Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med Image Anal. 2008;12:335–57.CrossRefPubMedGoogle Scholar
  2. Chan T, Vese L. Active contours without edges. IEEE Trans Image Process. 2001;10(2):266–77. ISSN 1057-7149. doi: 10.1109/83.902291.Google Scholar
  3. Courant R, Hilbert D. Methods of mathematical physics. vol 2. New York: Wiley; 1989.Google Scholar
  4. Dakua S, Sahambi J. Automatic left ventricular contour extraction from cardiac magnetic resonance images using cantilever beam and random walk approach. Cardiovasc Eng. 2010;10:30–43.CrossRefGoogle Scholar
  5. Doll N, Suwalski P, Aupperle H, Walther T, Borger MA, Schoon HA, Mohr FW. Endocardial laser ablation for the treatment of atrial fibrillation in an acute sheep model. J Card Surg. 2008;23(3):198–203.CrossRefPubMedGoogle Scholar
  6. Frangi A, Niessen W, Viergever M. Three-dimensional modeling for functional analysis of cardiac images, a review. IEEE Trans Med Imaging. 2001;20(1):2–5. ISSN 0278-0062. doi: 10.1109/42.906421.Google Scholar
  7. Grady L. Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2006;28(11):1768–83. ISSN 0162-8828. doi: 10.1109/TPAMI.2006.233.Google Scholar
  8. Satpathy A, Eng H, Jiang X. Difference of Gaussian edge-texture based background modeling for dynamic traffic conditions. In: LNCS, number 5358. ISVC 2008. p. 406–17.Google Scholar
  9. Smolka B, Szczepanski M, lataniotis K, Venetsanopoulos A. Random walk approach to noise reduction in images. In: LNCS, number 2124. New York: Springer; 2002. p. 527–36.Google Scholar
  10. Zhu X, Lafferty J, Ghahramani Z. Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In: ICML 2003 workshop on the continuum from labeled to unlabeled data in machine learning and data mining. 2003. p. 58–65.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringIndian Institute of TechnologyGuwahatiIndia

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