access icon free Greedy framework for optical flow tracking of myocardium contours

Optical flow (OF) tracking of the myocardium contours has a potential in segmenting the myocardium in time sequences of cardiac medical images. Nevertheless, to estimate the displacement field of the contour points, a number of assumptions are required to solve an under-determined set of optical flow equations. In this work, a new framework is proposed to solve the OF tracking problem using greedy optimisation algorithm. The new framework allows different types of constraints such as motion invariance, shape and topology to be applied in a unified way. The developed methods are applied to a publicly-available cardiac magnetic resonance imaging dataset containing image sequences for 33 patients. Quantitative evaluation of the results shows high potential of the methods to accurately track and segment the myocardium contours.

Inspec keywords: cardiology; image segmentation; greedy algorithms; optimisation; medical image processing; muscle; image sequences; biomedical MRI

Other keywords: image sequences; motion invariance; myocardium contours; cardiac magnetic resonance imaging; greedy optimisation algorithm; optical flow tracking; contour segmentation; cardiac medical images; time sequences

Subjects: Biomechanics, biorheology, biological fluid dynamics; Biology and medical computing; Optical, image and video signal processing; Biomedical magnetic resonance imaging and spectroscopy; Optimisation techniques; Optimisation techniques; Patient diagnostic methods and instrumentation; Medical magnetic resonance imaging and spectroscopy; Computer vision and image processing techniques

References

    1. 1)
      • 5. Attar, M., Osman, N.F., Fahmy, A.S.: ‘Myocardial segmentation using constrained multi-seeded region growing’. Int. Conf. Image Analysis and Recognition (ICIAR), Portugal, 2010.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 7. Al-Agamy, A.O., Osman, N.F., Fahmy, A.S.: ‘Segmentation of ascending and descending aorta from magnetic resonance flow images’. Proc. Fifth Cairo Int. Conf. Biomedical Engineering (CIBEC 2010), 2010, pp. 4144.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 19. Toennies, K.: ‘Validation. Guide to Medical Image Analysis’ (Springer London, 2012), pp. 413442.
    12. 12)
    13. 13)
    14. 14)
      • 10. Jinah, P., Metaxas, D., Axel, L.: ‘Deformable models with parameter functions for left ventricle 3-D wall motion analysis and visualization’. Computers in Cardiology 1995, 10–13 September 1995, pp. 241244.
    15. 15)
    16. 16)
    17. 17)
      • 15. Hamarneh, G., Althoff, K., Gustavsson, T.: ‘Snake deformations based on optical flow forces for contrast agent tracking in echocardiography’. Proc. Swedish Symp. Image Analysis (SSAB), 2000.
    18. 18)
      • 20. Alattar, M., Osman, N., Fahmy, A.: ‘Myocardial segmentation using constrained multi-seeded region growing’, Campilho, A., Kamel, M. (Eds.): ‘Image Analysis and Recognition’ (Springer Berlin Heidelberg, 2010), vol. 6112, pp. 8998.
    19. 19)
    20. 20)
    21. 21)
      • 21. Fahmy, A., Al-Agamy, A., Khalifa, A.: ‘Myocardial segmentation using contour-constrained optical flow tracking’, Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (Eds.): ‘Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges’ (Springer Berlin Heidelberg, 2012), vol. 7085, pp. 120128.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2012.0512
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