Automatic Landmark Location for Analysis of Cardiac MRI Images

  • Chrisina Jayne
  • Andreas Lanitis
  • Chris Christodoulou
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


This paper addresses the problem of automatic location of landmarks used for the analysis of MRI cardiac images. Typically the landmarks of shapes in MRI images are located manually which is a time consuming process requiring human expertise and attention to detail. As an alternative a number of researchers use shape modelling and image search techniques for locating the required landmarks automatically. Usually these techniques require human expertise for initializing the search and in addition they require high quality, noise free images so that the image-based landmark location is successful. With our work we propose the use of neural network methods for learning the geometry of sets of points so that it is possible to predict the positions of all required landmarks based on the positions of a small subset of the landmarks rather than using image-data during the process of landmark-location. As part of our work the performance of neural network methods like Multilayer Perceptrons, Radial Basis Functions and Support Vector Machines is evaluated. Quantitative and visual results demonstrate the potential of using such methods for locating the required landmarks on endo-cardial and epicardial landmarks of the left ventricle of MRI cardiac images.


MRI cardiac images automatic landmarks location neural networks 


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  1. 1.
    Nachtomy, E., Cooperstein, R., Vaturi, M., Bosak, E., Vered, Z., Akselrod, S.: Automatic assessment of cardiac function from short-axis MRI: procedure and clinical evaluation. Magn. Reson. Imaging 16(4), 365–376 (1998)CrossRefGoogle Scholar
  2. 2.
    Frangi, A.F., Niessen, W., Viergever, M.A.: Three-dimensional modelling for functional analysis of cardiac images: a review. IEEE Trans. Med. Imaging 20(91), 2–25 (2001)CrossRefGoogle Scholar
  3. 3.
    Andreopoulos, A., Tsotsos, J.K.: Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med. Imag. Anal. 12, 335–357 (2008)CrossRefGoogle Scholar
  4. 4.
    Eugene, C., Lin, M.D.: Cardiac MRI. Technical Aspects Primer (2011),
  5. 5.
    Petitjeana, C., Dacherb, J.-N.: A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis 15, 169–184 (2011)CrossRefGoogle Scholar
  6. 6.
    Cocosco, C.A., Niessen, W.J., Netsch, T., Vonken, E.J., Lund, G., Stork, A., Viergever, M.A.: Automatic image-driven segmentation of the ventricles in cardiac cine MRI. J. Magn. Reson. Imaging 28(2), 366–374 (2008)CrossRefGoogle Scholar
  7. 7.
    Mitchell, S.C., Lelieveldt, B.P., van der Geest, R.J., Bosch, H.G., Reiber, J.H., Sonka, M.: Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans. Med. Imaging 20(5), 415–423 (2001)CrossRefGoogle Scholar
  8. 8.
    Zhu, Y., Papademetris, X., Sinusas, A.J., Duncan, J.S.: Segmentation of the Left Ventricle From Cardiac MR Images Using a Subject-Specific Dynamical Model. IEEE T. on Medical Imaging 29(3), 660–687 (2010)Google Scholar
  9. 9.
    Hong, L., Huaifei, H., Xiangyang, X., Enmin, S.: Automatic LeftVentricleSegmentation in Cardiac MRI Using Topological Stable-State Thresholding and Region Restricted Dynamic Programming. Acad. Radiol. (2012),
  10. 10.
    Stalidis, G., Maglaveras, N., Efstratiadis, S., Dimitriadis, A., Pappas, C.: Model based processing scheme for quantitative 4-D cardiac MRI analysis. IEEE Trans. Inf. Technol. Biomed. 6(1), 59–72 (2002)CrossRefGoogle Scholar
  11. 11.
    Rumelhart, D.E., Hinton, D.E., Williams, R.J.: Learning representations by back-propagation errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  12. 12.
    Powell, M.J.D.: Radial basis functions for multivariable interpolation: A review. In: IMA Conference on Algorithms for the Approximation of Functions and Data, pp. 143–167. RMCS, Shrivenham (1985)Google Scholar
  13. 13.
    Vapnik, V.: Estimation of Dependences Based on Empirical Data. Moscow, Nauka (1982) (in Russian); English Translation: Springer, New York (1979)Google Scholar
  14. 14.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHCrossRefGoogle Scholar
  15. 15.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  16. 16.
    Moler, M.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6(4), 525–533 (1993)CrossRefGoogle Scholar
  17. 17.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)zbMATHGoogle Scholar
  18. 18.
    Gunn, S.R.: Support Vector Machines for Classification and Regression. Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton (1997)Google Scholar
  19. 19.
    Vapnik, V., Golowich, S., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. In: Mozer, M., Jordan, M., Petsche, T. (eds.) Neural Information Processing Systems, pp. 169–184. MIT Press, Cambridge (1997)Google Scholar
  20. 20.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Pérez-Cruz, F., Camps-Valls, G., Soria-Olivas, E., José Pérez-Ruixo, J., Figueiras-Vidal, A.R., Artés-Rodríguez, A.: Multi-dimensional Function Approximation and Regression Estimation. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 757–762. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Sánchez-Fernández, M., de Prado-Cumplido, M., Arenas-García, J., Pérez-Cruz, F.: SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Trans. Signal Proc. 52(8), 2298–2307 (2004)CrossRefGoogle Scholar
  23. 23.
    Tuia, D., Verrelst, J., Alonso, L., Pérez-Cruz, F., Camps-Valls, G.: Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters 8(4), 804–808 (2011)CrossRefGoogle Scholar
  24. 24.
    Jayne, C., Lanitis, A., Christodoulou, C.: Neural network methods for one-to-many multi-valued problems. Neural Computing and Applications 20, 775–785 (2011)CrossRefGoogle Scholar
  25. 25.
    Gower, J.: Generalized procrustes analysis. Psychometrika 40(1), 33–51 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference and Prediction. Springer (2001)Google Scholar
  27. 27.
    Cherkassky, V., Shao, X., Mulier, F., Vapnik, V.: Model Complexity Control for Regression Using VC Generalization Bounds. IEEE T. on Neural Networks 10(5), 1075–1089 (1999)CrossRefGoogle Scholar
  28. 28.
    Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17(1), 113–126 (2004)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chrisina Jayne
    • 1
  • Andreas Lanitis
    • 2
  • Chris Christodoulou
    • 3
  1. 1.Department of ComputingCoventry UniversityCoventryUK
  2. 2.Department of Multimedia and Graphic ArtsCyprus University of TechnologyLemesosCyprus
  3. 3.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

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