Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images
DOI:
https://doi.org/10.24297/ijct.v15i11.4376Keywords:
Cardiovascular diseases (CVDs), Segmentation, Active contour, Registration, Level set initialization, Perfusion MRIAbstract
Cardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentation is needed for CVDs early detection. In this paper, we study the effect of the registration and initialization of the level set segmentation on the performance of extracting the heart ventricles for the short axis cardiac perfusion MRI images, as a result, we propose a modified workflow to automatically segment the ventricles by mitigating the levelset initial contour extraction in order to improve the segmentation results accuracy. In the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established based on the phase correlation methodology. As for the segmentation experiments, the level set initialization was done through extracting the ventricles’ real shape from each slice. Though, the final contour of any frame will be used as the initial contour for the next frame. The second initialization strategy was based on defining the initial contour for each frame using the polar representation of the image. Two short axis view datasets of cardiac magnetic resonance (CMR) perfusion imaging were used in testing the proposed methods. Dice coefficient, sensitivity, specificity and Hausdorff distance have been used to evaluate and validate the segmentation results. The system workflow consists of five main modules: preprocessing, localization, initial contour extraction, registration, and segmentation. The segmentation accuracy for left and right ventricles improved from 72% to 77% and from 70% to 81% using the spatial domain based registration algorithm. The polar-based initialization strategy improves the segmentation accuracy from 77% to 81% and from 81% to 82% for the left and right ventricles respectively.Downloads
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References
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14. Ringenberg, J., Deo, M., Devabhaktuni, V., Berenfeld, O., Boyers, P., and Gold, J., Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI, Comput. Med. Imaging Graph., vol. 38, no. 3, pp. 190–201, Apr. 2014.
2. McRobbie, D. , Moore, E., Graves, M., Prince, M. 2007 MRI: From Picture to Proton, Cambridge university press.
3. Grosgeorge, D., Petitjean, C., Caudron, J., Fares, J., Dacher, J. N. 2011 Automatic cardiac ventricle segmentation in MR images: A validation study, Int. J. Comput. Assist. Radiol. Surg., vol. 6, no. 5, 573–581.
4. Pluempitiwiriyawej, C., Sotthivirat, S. 2005 Active contours with automatic initialization for myocardial perfusion analysis, Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 3, pp. 3332–3335.
5. Shen, W., Kassim, A. a 2007 “A new scheme for automatic initialization of deformable models,†IEEE Int. Conf. Image Process, vol. 4, pp. IV-289.
6. Wu, Y., Wang, Y., and Jia, Y. 2013 Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model, Comput. Vis. Image Underst., vol. 117, no. 9, pp. 990–1003.
7. Li, C., Xu, C., Gui, C., and Fox, M. D. 2010 Distance regularized level set evolution and its application to image segmentation, IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243–3254.
8. Khalifa, F., Beache, G. M., Elnakib, a., Sliman, Gimel’farb, H. G., Welch, K. C., El-Baz, a. 2013 A new shape-based framework for the left ventricle wall segmentation from cardiac first-pass perfusion MRI, IEEE 10th Int. Symp. Biomed. Imaging, pp. 41–44.
9. Mousa, D., Zayed, N., and Yassine, I. 2016 The Impact of Polar based initialization and frame time curve selection on Left Ventricle short axis Perfusion MR Segmentation, ISMRM 24th Annual Meeting & Exhibition, SMRT 25th Annual Meeting Conf..
10. Sarode, M. V, and Deshmukh, D. P. R. 2010 Performance evaluation of rician noise reduction algorithm in magnetic resonance imaging, Journal of Emerging Trends in Computing and Information Sciences., vol. 2, pp. 39–44, .
11. Mousa, D., Zayed, N., and Yassine, I. 2014 Automatic cardiac MRI localization method, Cairo Inter. Biomed. Eng. Conf., pp. 153–157.
12. Yan, H., and Liu, J. G., 2008 Robust phase correlation based feature matching for image co-registration and DEM generation, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., no. B7, pp. 1751–1756.
13. Baswaraj, B. D., Govardhan, a, and Premchand, P. 2012 “Active contours and image segmentation: the current state of the art,†Glob. J. Comput. Sci. Technol. Graph. Vis., vol. 12, no. 11.
14. Ringenberg, J., Deo, M., Devabhaktuni, V., Berenfeld, O., Boyers, P., and Gold, J., Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI, Comput. Med. Imaging Graph., vol. 38, no. 3, pp. 190–201, Apr. 2014.
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Published
2016-08-18
How to Cite
Mousa, D., Zayed, N., & Yassine, I. A. (2016). Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(11), 7218–7226. https://doi.org/10.24297/ijct.v15i11.4376
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Research Articles
