Left Ventricle Segmentation in Magnetic Resonance Images with Modified Active Contour Method

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Author(s)

Maryam Aghai Amirkhizi 1,* Siyamak Haghipour 1

1. Department of Mechanical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2013.10.03

Received: 7 May 2013 / Revised: 3 Jun. 2013 / Accepted: 27 Jun. 2013 / Published: 8 Aug. 2013

Index Terms

Left ventricle Segmentation, MRI, Active contours, Dice metric, point to curve

Abstract

Desired segmentation of the image is a pivotal problem in image processing. Segmenting the left ventricle (LV) in magnetic resonance images (MRIs) is essential for evaluation of cardiac function. For the segmentation of cardiac MRI several methods have been proposed and implemented. Each of them has advantages and restrictions. A modified region-based active contour model was applied for segmentation of LV chamber. A new semi-automatic algorithm was suggested calculating the appropriate Balloon force according to mean intensity of the region of interest for each image. The database is included of 2,039 MR images collected from 18 children under 18. The results were compared with previous literatures according to two standards: Dice Metric (DM) and Point to Curve (P2C). The obtained segmentation results are better than previously reported values in several literatures. In this study different points were used in cardiac cycle and several slice levels and classified into three levels: Base, Mid. and Apex. The best results were obtained at end diastole (ED) in comparison with end systole (ES), and on base slice than other slices, because of LV bigger size in ED phase and base slice. With segmentation of LV MRI based on novel active contour and application of the suggested algorithm for balloon force calculation, the mean improvement of DM compared to Grosgeorge et al. is 19.6% in ED and 49.5% in ES phase. The mean improvement of P2C compared with the same literature respectively for ED and ES phase is 43.8% and 39.6%. 

Cite This Paper

Maryam Aghai Amirkhizi, Siyamak Haghipour,"Left Ventricle Segmentation in Magnetic Resonance Images with Modified Active Contour Method", IJIGSP, vol.5, no.10, pp.19-25, 2013. DOI: 10.5815/ijigsp.2013.10.03

Reference

[1]R. El Berbari, I. Bloch, A. Redheuil, E. Angelini, E. Mousseaux, F. Frouin, A. Herment,"An automated myocardial segmentation in cardiac MRI", IEEE Engineering in Medicine & Biology Society, Lyon, France, 2007, pp.4508-4511.

[2]D. Grosgeorge, C. Petitjean, J. Caudron, J. Fares, J-N. Dacher, "Automatic Cardiac Ventricle Segmentation in MR Images: a Validation Study", International Journal of Computer Assisted Radiology and Surgery, vol. 19, No. 8, 2010, pp. 991-1002.

[3]G. Narayan, K. Nayak, J. Pauly, B. Hu," Single-breathhold, four-dimensional, quantitative assessment of LV and RV function using triggered, real-time, steady-state free precession MRI in heart failure patients", Journal of Magnetic Resonance Imaging, 2005, pp. 59–66.

[4]A. Andreopoulos, J. K. Tsotsos, "Efficient and Generalizable Statistical Models of Shape and Appearance for Analysis of Cardiac MRI", Medical Image Analysis, vol. 12,No. 3, 2008, pp. 335-357.

[5]M. Kass, A. Witkin, D. Terzopoulos, "Snakes:active contour models", International Journal of Computer Vision", vol. 1, 1988, pp. 321-331.

[6]N., Ahuia, N., Xu, R., Bansal,"Object segmentation using graph cuts based active contours", Computer Vision and Image Understanding ", vol. 107, 2007, pp. 210-224.

[7]V. Caselles, R. Kimmel, G. Sapiro, "Geodesic active contours", in Proceedings of IEEE International Conference on Computer Vision, Boston, MA., 1995, pp. 694-699.

[8]V. Caselles, R. Kimmel, G. Sapiro, "Geodesic active contours", International Journal of Computer Vision, vol. 22, No. 1, 1997, pp. 61-79.

[9]T. Chan, L. Vese, (2001) "Active contours without edges", IEEE Transaction on Image Processing, vol. 10, No. 2, pp. 266–277.

[10]G. P. Zhu, Sh. Q. Zhang, Q. SH. Zeng, Ch. H., Wang, "Boundary-based image segmentation using binary level set method", Optical Engineering, vol. 46, 2007.

[11]K.. Zhang, L. Zhang, H. Song, W. Zhou. (2010) "Active contours with selective local or global segmentation: A new formulation and level set method", Image and Vision Computing, vol. 28, pp. 668-676.

[12]C. M. Li, C. Y. Xu, C. F. Gui, M. D. Fox," level set evolution without re-initialization:a new variational formulation", in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, pp. 430-436.

[13]N. Paragios, R. Deriche, "Geodesic active contours and level sets for detection and tracking of moving objects", IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, 2000, pp. 1-15.

[14]C. Y. Xu, A. Yezzi, J. L. Prince, "On the relation between parametric and geometric active contours". in processing of 34th asilomar conference on signals and computers, 2000.

[15]A. Vasilevskiy, K. Siddiqi, "Flux-maximizing geometric flows", IEEE transaction on pattern analysis and machine intelligence, vol. 24, 2002, pp. 1565-1578.

[16]J. Lie, M. Lysaker, X. C. Tai, (2006) "A binary level set model and some application to Mumford-Shah image segmentation", IEEE Transaction on Image Processing, vol. 15, 2006, pp.1171-1181.

[17]D. Mumford, J. Shah, "Optimal approximation by piecewise smooth function and associated variational problems", Communication on Pure and Applied Mathematics, vol. 42, 1989, pp. 577-685.

[18]C. M. Li, C. Kao, J. Gore, Z. Ding, "Implicit active contours driven by local binary fitting energy", in IEEE Conference on Computer Vision and Pattern Recognition, 2007.

[19]A. Tsai, A. Yezzi, A. S. Willsky, "Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification", IEEE Transaction on Image Processing, vol. 10, 2001, pp. 1169-1186.

[20]L. A. Vese, T. F. Chan, "A multiphase level set framework for image segmentation using the Mumford-Shah model", International Journal of Computer Vision", vol. 50, 2002, pp. 271-293.

[21]R. Ronfard,"Region-based strategies for active contour models", International Journal of Computer Vision, vol. 46, 2002, pp. 223-247.

[22]N., Paragios, R., Deriche, "Geodesic active regions and level set methods for supervised texture segmentation", International Journal of Computer Vision, vol. 46, 2002, pp. 223-247.

[23]S. Osher, R. Fedkiw, Level Set Method and Dynamic Implicit Surfaces, Applied Mathematical Sciences, Vol. 153, Springer-Verlag, New York, 2002 .

[24]G. Aubert- P. Kornprobst ,"Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations", Springer Verlag, Applied Mathematical Sciences, vol. 147, 2001.

[25]C. Li, C. Kao, J. C. Gore, Z. Ding, "Minimization of region-scalable fitting energy for image segmentation", in IEEE Transaction on Image Processing, 2008, pp. 1940-1949.

[26]M.D. Cerqueira, N. J. Weissman, V., Dilsizian, A.K., Jacobs, S., Kaul, WK., Laskey, D. J., Pennell, J.A., Rumberger, T., Ryan, M. S., Verani, "Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart", Circulation, vol. 105, No. 4, 2002, pp.539-542.

[27]M. Lynch, O. Ghita, P. Whelan, "Automatic segmentation of the left ventricle cavity and myocardium in MRI data", Computers in bilogy and medicine, vol. 36, No. 4, 2006, pp. 389-407.

[28]M. R. Kaus, J. Von Berg, J. Weese, W. Niessen,V. Pekar, "Automated segmentation of the left ventricle in cardiac MRI, Medical Image Analysis, vol. 8, No. 3, 2004, pp. 245-254.

[29]S. Mitchell, B. Lelieveldt, R. Van der Geest, J. Bosch, J. Reiber, M. Sonka, "Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images", IEEE Transactions of Medical Imaging, vol. 20, No. 5, 2001, pp. 415-423.

[30]S. Mitchell, J. G. Bosch, B. Lelieveldt, R. J. Van der Geest, J. H. C. Reiber, M. Sonka, "3D active appearance models: segmentation of cardiac MR and ultrasound images", IEEE Transactions of Medical Imaging, vol. 21, No. 9, 2002, pp. 1167-1178.

[31]H. C. Van Assen, M. H. Danilouchkine, A.F. Frangi, S. Orda, Westen berg, J.J. Reiber, J.H. Lelieveldt, B.P. "SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data", Medical Image Analysis, vol. 10, 2006, pp. 286-303.

[32]M. Lorenzo-Valdes, G. I. Sanchez-Ortiz, A. G. Elkington, R. H. Mohiaddin, D. Rueckert, "Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm", Meical Image Analysis, vol. 8, No. 3, 2004, pp. 255-265.

[33]J. Lötjönen, S. Kivistö, J. Koikkalainen, D. Smutek, K. Lauerma, "Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images", Medical Image Analysis, vol. 8, No. 3, 2004, pp. 371-386.