Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
A strain analysis is a novel diagnostics method used in the cardiology. The advanced regional quantitive analysis of the myocardium during a systole and a diastole allows to diagnose a cardiac cycle. Realization of the analysis requires a myocardial segmentation algorithm. In this paper the myocardial segmentation method from the cardiovascular magnetic resonance sequences (CMR) has been presented. The endocardium areas are calculated using active contour algorithm with the gradient vector flow forces (GVF). Apart from that, the algorithm uses fuzzy logic approach to detect the edges. As a result, the curve matches to image contents. The epicardium boundaries are being designated and supplemented by the surrounding analysis and Fourier descriptors. Based on the endocardium and the epicardium boundaries which limit the myocardium it is possible to realize analysis a local stenosis detection and the directional strain during the cardiac cycle. The analysis is based on the CMR images of the left ventricle which were acquired in short axis of left ventricle and radial direction. The most important achievements presented in this paper are fuzzy logic application in the image processing, the active contour segmentation method improvements and the formal descriptions of the myocardium boundaries.
Czasopismo
Rocznik
Tom
Strony
85--90
Opis fizyczny
Bibliogr. 19 poz., tab., wykr.
Twórcy
autor
- AGH University of Science and Technology, Institute of Automatics, Bio-Cybernetics Laboratory, al. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
- 1. Shin I. et al. (2002), Segmentation and visualization of left ventricle in MR Cardiac images, International Conference on Image Processing, Vol 2, II-89-II-92.
- 2. Kass M. et al., Snakes: active contour models, International Journal of Computer Vision l, 321-331.
- 3. Xu C., Prince J. L. (1998), Generalized Gradient Vector Flow External Forces for Active Contours, Signal Processing An International Journal, 71(2), 131-139.
- 4. Ciofolo C. et al. (2008), Automatic myocardium segmentation in late-enhacement MRI, 5th IEEE International Symposium on Biomedical Imaging, 225-228.
- 5. Carranza N. et al. (2008), Motion Estimation and Segmentation of Cardiac Magnetic Resonance Images Using Variational and Level Set Techniques, 16th European Signal Processing Conference.
- 6. Jie Zhu-Jacquot Zabih R. (2008), Segmentation of the left ventricle in cardiac MR images using graph cuts with parametric shape priors, IEEE International Conference on Acoustics, Speech and Signal Processing, 521-524.
- 7. Mitchell S. C. et al. (2002), 3-D active appearance models: segmentation of cardiac MR and ultrasound images, IEEE Transactions on Medical Imaging 21(9), 1167-1178.
- 8. van Assen H. C. et al. (2008), A 3-D Active Shape Model Driven by Fuzzy Inference: Application to Cardiac CT and MR, IEEE Transactions on Information Technology in Biomedicine 12(5), 595-605.
- 9. Wang G. et al. (2009), A Novel Segmentation Method for Left Ventricular from Cardiac MR Images Based on Improved Markov Random Field Model, 2nd International Congress on Image and Signal Processing, 1-5.
- 10. Hae-Yeoun L. et al. (2010), Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI, IEEE Transactions on Biomedical Engineering, 57(4), 905-913.
- 11. El Berbari R. et al. (2007), An automated myocardial segmentation in cardiac MRI, In Proceedings of the 29th Annual International Conference of the IEEE EMBS.
- 12. Xu Ch., Prince J. L. (1997), Gradient Vector Flow: A New External Force for Snakes, pp. 66, 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'97), 66.
- 13. Xu Ch., Prince J. L. (1998), Snakes, Shapes, and Gradient Vector Flow, IEEE Transactions on Image Processing 7/3.
- 14. McInerney T., Terzopoulos D. (2009), Deformable Models, In I. N. Bankman, Handbook of Medical Image Processing and Analysis, Elsevier.
- 15. Das B., Banerjee S. (2007), Parametric Contour Model in Medical Image Segmentation, In J. S. Suri, A. Farag Eds., Deformable Models II: Theory & Biomedical Applications, Springer.
- 16. Barkhoda W. et al. (2009), Fuzzy Edge Detection Based on Pixel’s Gradient and Standard Deviation Values, Proceedings of the International Multi Conference on Computer Science and Information Technology, 7 – 10.
- 17. Acton S. T., Ray N. (2009), Biomedical Image Analysis: Segmentation, Morgan & Claypool Publishers.
- 18. Gonzales R. C., Woods R. E. (2008), Digital Image Processing, Pearson Education.
- 19. Suri J. S. et al. (2002), Advanced Algorithmic Approaches to Medical Image Segmentation. State-of-the-Art Applications in Cardiology, Neurology, Mammography and Pathology. Advances in Pattern Recognition, Springer-Verlag, London.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6ce193e2-6b8f-4841-9c4f-d39d0be896b3