© The Institution of Engineering and Technology
The quantitative analysis of the left ventricle (LV) contractile function is one of the key steps in the assessment of cardiovascular disease. Such analysis greatly depends on the accurate delineation of LV boundary from cardiac sequences. However, segmentation of the LV still remains a challenging problem due to its subtle boundary, occlusion, and image inhomogeneity. To overcome such difficulties, the authors propose a novel segmentation method by incorporating a dynamic shape constraint into the weighting function of the random walks segmentation algorithm. This approach involves iterative updates on the intermediate result to achieve the desired solution. The inclusion of a shape constraint restricts the solution space of the segmentation result to handle misleading information that may come from noise, weak boundaries and clutter, leading to increased robustness of the algorithm. The authors describe the details of the proposed method and demonstrate its effectiveness in segmenting the LV from real cardiac magnetic resonance (CMR) image sets. The experimental results demonstrate that the proposed method obtains better segmentation performance than the standard method.
References
-
-
1)
-
5. Lorenzo-Valdés, M., Sanchez-Ortiz, G., Elkington, A., et al: ‘Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm’, Med. Image Anal., 2004, 8, (3), pp. 255–265 (doi: 10.1016/j.media.2004.06.005).
-
2)
-
6. Frangi, A., Niessen, W., Viergever, M.: ‘Three dimensional modelling for functional analysis of cardiac images: a review’, IEEE Trans. Med. Imaging, 2001, 20, (1), pp. 2–25 (doi: 10.1109/42.906421).
-
3)
-
16. Cremers, D., Tischhauser, F., Weickert, J., et al: ‘Diffusion snakes: introducing statistical shape knowledge into the Mumford–Shah functional’, Int. J. Comput. Vis., 2002, 50, (3), pp. 295–313 (doi: 10.1023/A:1020826424915).
-
4)
-
4. Mitchell, S.C., Lelieveldt, B.P., van der Geest, R.J., et al: ‘Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images’, IEEE Trans. Med. Imaging, 2001, 20, (5), pp. 415–423 (doi: 10.1109/42.925294).
-
5)
-
41. Lu, Y., Radau, P., Connelly, K., et al: ‘Automatic image-driven segmentation of left ventricle in cardiac cine MRI’. MICCAI 2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge, MIDAS Journal, 2009. .
-
6)
-
19. Grosgeorge, D., Petitjean, C., Dacher, J., et al: ‘Graph cut segmentation with a statistical shape model in cardiac MRI’, Comput. Vis. Image Underst., 2013, 117, (9), pp. 1027–1035 (doi: 10.1016/j.cviu.2013.01.014).
-
7)
-
38. Schaerer, J., Casta, C., Pousin, J., et al: ‘A dynamic elastic model for segmentation and tracking of the heart in MR image sequences’, Med. Image Anal., 2010, 14, (6), pp. 738–749 (doi: 10.1016/j.media.2010.05.009).
-
8)
-
2. O'Brien, S.P., Ghita, O., Whelan, P.F.: ‘A novel model-based 3D + time left ventricular segmentation technique’, IEEE Trans. Med. Imaging, 2011, 30, (2), pp. 461–474 (doi: 10.1109/TMI.2010.2086465).
-
9)
-
23. Wu, Y., Wang, Y., Jia, Y.: ‘Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model’, Comput. Vis. Image Underst., 2013, 117, (9), pp. 990–1003 (doi: 10.1016/j.cviu.2012.12.008).
-
10)
-
25. Ben Ayed, I., Li, S., Ross, I.: ‘Embedding overlap priors in variational left ventricle tracking’, IEEE Trans. Med. Imaging, 2009, 28, (12), pp. 1902–1913 (doi: 10.1109/TMI.2009.2022087).
-
11)
-
22. Cui, H., Wang, X., Fulham, M., et al: ‘Prior knowledge enhanced random walk for lung tumor segmentation from low-contrast CT images’. Proc. of EMBS 2013, 2013, pp. 6071–6074.
-
12)
-
18. Mahapatra, D., Sun, Y.: ‘Orientation histograms as shape priors for left ventricle segmentation using graph cuts’. Proc. of MICCAI 2011, 2011, vol. 6893, pp. 420–427.
-
13)
-
15. Mahapatra, D.: ‘Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors’, J. Digit. Imaging, 2013, 26, (4), pp. 721–730 (doi: 10.1007/s10278-012-9548-5).
-
14)
-
32. Queirós, S., Barbosa, D., Heyde, , et al: ‘Fast automatic myocardial degmentation in 4D cine CMR datasets’, Med. Image Anal., 2014, 18, (7), pp. 1115–1131 (doi: 10.1016/j.media.2014.06.001).
-
15)
-
14. Sun, W., Cetin, M., Chan, R., et al: ‘Learning the dynamics and time-recursive boundary detection of deformable objects’, IEEE Trans. Image Process., 2008, 17, (11), pp. 2186–2200 (doi: 10.1109/TIP.2008.2004638).
-
16)
-
30. Grady, L.: ‘Random walks MATLAB implementation source codes’. .
-
17)
-
26. Ben Ayed, I., Chen, H., Punithakumar, K., et al: ‘Max-flow segmentation of the left ventricle by recovering subject-specific distributions via a bound of the Bhattacharyya measure’, Med. Image Anal., 2012, 16, (1), pp. 87–100 (doi: 10.1016/j.media.2011.05.009).
-
18)
-
17. Pluempitiwiriyawej, C., Moura, J., Wu, Y., et al: ‘Stacs: new active contour scheme for cardiac mr image segmentation’, IEEE Trans. Med. Imaging, 2005, 24, (5), pp. 593–603 (doi: 10.1109/TMI.2005.843740).
-
19)
-
29. Yang, X., Yeo, S.Y., Lim, C., et al: ‘A framework for auto-segmentation of left ventricle from magnetic resonance images’. Proc. of APCOM&ISCM 2013, 2013, .
-
20)
-
43. Lynch, M., Ghita, O., Whelan, P.F.: ‘Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model’, IEEE Trans. Med. Imaging, 2008, 27, (2), pp. 195–203 (doi: 10.1109/TMI.2007.904681).
-
21)
-
28. Yang, X., Su, Y., Wan, M., et al: ‘Left ventricle segmentation by dynamic shape constrained random walks’. Proc. of EMBS 2014, 2014, pp. 4723–4726.
-
22)
-
8. Boykov, Y., Funka-Lea, G.: ‘Graph cuts and efficient N–D image segmentation’, Int. J. Comput. Vis., 2006, 70, (2), pp. 109–131 (doi: 10.1007/s11263-006-7934-5).
-
23)
-
42. Huang, S., Liu, J., Lee, L.C., et al: ‘Segmentation of the left ventricle from cine MR images using a comprehensive approach’. MICCAI 2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge, MIDAS Journal, 2009. .
-
24)
-
34. Constantinides, C., Roullot, E., Lefort, M., et al: ‘Fully automated segmentation of the left ventricle applied to cine MR images: description and results on a database of 45 subjects’. Proc. of EMBS 2012, 2012, pp. 3207–3210.
-
25)
-
36. Liu, H., Hu, H., Xu, X., et al: ‘Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming’, Acad. Radiol., 2012, 19, (6), pp. 723–731 (doi: 10.1016/j.acra.2012.02.011).
-
26)
-
35. Uzunbas, M., Zhang, S., Pohl, K., et al: ‘Segmentation of myocardium using deformable regions and graph cuts’. Proc. of ISBI 2012, 2012, pp. 254–257.
-
27)
-
9. Boykov, Y., Jolly, M.P.: ‘Interactive organ segmentation using graph cuts’, Lect. Notes Comput. Sci., 2000, 1935, pp. 276–286 (doi: 10.1007/978-3-540-40899-4_28).
-
28)
-
1. Kilner, P.J., Geva, T., Kaemmerer, H., et al: ‘Recommendations for cardiovascular magnetic resonance in adults with congenital heart disease from the respective working groups of the European Society of Cardiology’, Eur. Heart J., 2010, 31, (7), pp. 794–805 (doi: 10.1093/eurheartj/ehp586).
-
29)
-
24. Woo, J., Slomka, P.J., Jay Kuo, C.-C., et al: ‘Multiphase segmentation using animplicit dual shape prior: application to detection of left ventricle in cardiac MRI’, Comput. Vis. Image Underst., 2013, 117, (9), pp. 1084–1094 (doi: 10.1016/j.cviu.2012.11.012).
-
30)
-
20. Baudin, P.Y., Azzabou, N., Carlier, P.G., et al: ‘Prior knowledge, random walks and human skeletal muscle segmentation’. Proc. of MICCAI 2012, 2012, vol. 7510, pp. 569–576.
-
31)
-
10. Grady, L., Funka-Lea, G.: ‘Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials’, Lect. Notes Comput. Sci., 2004, 3117, pp. 230–245 (doi: 10.1007/978-3-540-27816-0_20).
-
32)
-
39. Jolly, M.P.: ‘Fully automatic left ventricle segmentation in cardiac cine MR images using registration and minimum surfaces’. MICCAI 2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge, MIDAS Journal, 2009. .
-
33)
-
13. Dakua, S.P., Sahambi, J.S.: ‘Modified active contour model and random walk approach for left ventricular cardiac MR image segmentation’, Int. J. Numerical Methods Biomed. Eng., 2011, 27, (9), pp. 1350–1361.
-
34)
-
27. Radau, P., Lu, Y., Connelly, K., et al: ‘Evaluation framework for algorithms segmenting short axis cardiac MRI’. MICCAI 2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge, MIDAS Journal, 2009. .
-
35)
-
31. Hu, H., Gao, Z., Liu, L., et al: ‘Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques’, PLoS One, 2014, 9, (12), p. e114760 (doi: 10.1371/journal.pone.0114760).
-
36)
-
21. Baudin, P.Y., Azzabou, N., Carlier, P.G., et al: ‘Manifold-enhanced segmentation through random walks on linear subspace priors’. Proc. of BMVC 2012, 2012, pp. 1–10.
-
37)
-
11. Grady, L.: ‘Random walks for image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (11), pp. 1–17 (doi: 10.1109/TPAMI.2006.233).
-
38)
-
40. Wijnhout, J., Hendriksen, D., Van Assen, H., et al: ‘LV challenge LKEB contribution: fully automated myocardial contour detection’. MICCAI 2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge, MIDAS Journal, 2009. .
-
39)
-
12. Eslami, A., Karamalis, A., Katouzian, A., et al: ‘Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI’, Med. Image Anal., 2013, 17, (2), pp. 236–253 (doi: 10.1016/j.media.2012.10.005).
-
40)
-
7. Petitjean, C., Dacher, J.N.: ‘A review of segmentation methods in short axis cardiac MR images’, Med. Image Anal., 2011, 15, (2), pp. 169–184 (doi: 10.1016/j.media.2010.12.004).
-
41)
-
37. Huang, S., Liu, J., Lee, L., et al: ‘An image based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images’, J. Digit. Imaging, 2011, 24, (4), pp. 598–608 (doi: 10.1007/s10278-010-9315-4).
-
42)
-
33. Hu, H., Liu, H., Gao, Z., et al: ‘Hybrid segmentation of left ventricle in cardiac MRI using Gaussian-mixture model and region restricted dynamic programming’, Magn. Reson. Imaging, 2013, 31, (4), pp. 575–584 (doi: 10.1016/j.mri.2012.10.004).
-
43)
-
3. Weng, J., Singh, A., Chiu, M.: ‘Learning-based ventricle detection from cardiac MR and CT images’, IEEE Trans. Med. Imaging, 1997, 16, (4), pp. 378–391 (doi: 10.1109/42.611346).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2014.0450
Related content
content/journals/10.1049/iet-cvi.2014.0450
pub_keyword,iet_inspecKeyword,pub_concept
6
6