Automatic Segmentation Measuring Function for Cardiac MR-Left Ventricle (LV) Images


  • D.N.F. Awang Iskandar Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • A. Khan Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • P.C. Lim Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • Y.C. Wang Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.


VLSM, Sign Euclidean Distance Function, Fuzzy C Mean Interaction Operator, Segmentation Error,


Automatic segmentation approaches are a desirable solution for Endocardium (inner) and Epicardium (outer) contours delineation using cardiac magnetic resonance left ventricle (CMR-LV) short axis images. The Level Set Model (LSM) and Variational LSM (VLSM) is the state-of-the-art in detecting the inner and outer contour for medical images. However, in CMR-LV images segmentation the LSM and VLSM are facing with the issue of re-initialisation because of irregular circle shape. In this paper, we developed an automatic segmentation measuring function based on statistical formulation to solve the re-initialisation issues in huge set of data images. The sign Euclidean distance function successfully classified the negative (inner contour) and positive (outer contour) features. The Fuzzy C mean interaction operator intersects the high membership degree that initialises the centre point. The experiments were conducted using the Sunnybrook and Pusat Juntung Hospital Umum Sarawak (PJHUS) cardiac datasets. This paper aims at developing a distance function to guide the automatic segmentation for LV contours and also to reduce segmentation error.


Üzümcü, M., van der Geest, R. J., Swingen, C., Reiber, J. H., & Lelieveldt, B. P. (2006). Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming. Investigative radiology, 41(1), pp. 52-62.

Auger, D. A., Zhong, X., Epstein, F. H., Meintjes, E. M., & Spottiswoode, B. S. (2014). Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance, 16:8.

Bessa, J. A., Cortez, P. C., da Silva Félix, J. H., da Rocha Neto, A. R., & de Alexandria, A. R. (2015). Radial snakes: comparison of segmentation methods in synthetic noisy images. Expert Systems with Applications, 42(6), pp. 3079-3088.

Gering, D. T. (2003, November). Automatic segmentation of cardiac MRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Berlin Heidelberg, pp. 524-532.

Kaus, M. R., von Berg, J., Weese, J., Niessen, W., & Pekar, V. (2004). Automated segmentation of the left ventricle in cardiac MRI. Medical image analysis, 8(3), pp.245-254.

El Berbari, R., Bloch, I., Redheuil, A., Angelini, E., Mousseaux, E., Frouin, F., & Herment, A. (2007). An automated myocardial segmentation in cardiac MRI. IEEE Engineering in Medicine and Biology Society, pp. 4508-4511.

Liu, Y., Li, C., Guo, S., Song, Y., & Zhao, Y. (2014). A novel level set method for segmentation of left and right ventricles from cardiac MR images. IEEE Engineering in Medicine and Biology Society, pp.4719-4722.

Chen, S. Y., & Guan, Q. (2011). Parametric shape representation by a deformable NURBS model for cardiac functional measurements. IEEE Transactions on Biomedical Engineering, 58(3), pp. 480-487.

Perry, R. (2009). Cardiac MR Left Ventricle Segmentation Challenge. World Wide Web, http://smial. sri. utoronto. ca/LV Challenge/Home. html.

Ji, D., Yao, Y., Yang, Q., & Chen, X. (2016). MR Image Segmentation Using Graph Cuts Based Geodesic Active Contours. International Journal of Hybrid Information Technology, 9(1), pp. 91-100.

Gao, H., Kadir, K., Payne, A. R., Soraghan, J., & Berry, C. (2013). Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR. Journal of Cardiovascular Magnetic Resonance, pp. 15- 28.

Kadir, K., Gao, H., Payne, A., Soraghan, J., & Berry, C. (2011). Variational level set method with shape constraint and application to oedema cardiac magnetic resonance image. 17th International Conference. Digital Signal Processing (DSP), pp. 1-5.

Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of computational physics, 79(1), pp. 12-49.

Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on pure and applied mathematics, 42(5), pp.577-685.

Li, C., Xu, C., Konwar, K. M., & Fox, M. D. (2006). Fast distance preserving level set evolution for medical image segmentation. 9thIEEE International Conference, In Control Automation, Robotics and Vision, pp. 1-7

Li, C., Xu, C., Gui, C., & Fox, M. D. (2010). Distance regularized level set evolution and its application to image segmentation. IEEE transactions on image processing, 19(12), pp. 3243-3254.

Gupta, S., & Kumar, S. (2012). Variational level set formulation and filtering techniques on ct images. International Journal of Engineering Science and Technology (IJEST), 4(07), pp. 3509-3513.

Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on image processing, 10(2), pp. 266-277.

Khan, A., Iskandar, D. A., Ujir, H., & Chai, W. Y. (2017). Automatic Segmentation of CMRIs for LV Contour Detection. 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Springer Singapore, pp. 313-319.

R P. Radau, Y. Lu, K. Connelly, G. Paul, A. J. Dick, and G. A. Wright. (2009). Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS Cardiac MR Left Ventricle Segmentation Challenge, [Online]. Available:

Liao, W., Deserno, T. M., & Spitzer, K. (2008, March). Evaluation of free non-diagnostic DICOM software tools. In Medical Imaging (pp. 691903-691903). International Society for Optics and Photonics.




How to Cite

Awang Iskandar, D., Khan, A., Lim, P., & Wang, Y. (2017). Automatic Segmentation Measuring Function for Cardiac MR-Left Ventricle (LV) Images. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-10), 165–171. Retrieved from