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Endo- and Epi-cardial Boundary Detection of the Left Ventricle Using Intensity Distribution and Adaptive Gradient Profile in Cardiac CT Images  

Lee, Min-Jin (서울여자대학교 컴퓨터학과)
Hong, Helen (서울여자대학교 미디어학부)
Publication Information
Journal of KIISE:Software and Applications / v.37, no.4, 2010 , pp. 273-281 More about this Journal
Abstract
In this paper, we propose an automatic segmentation method of the endo- and epicardial boundary by using ray-casting profile based on intensity distribution and gradient information in CT images. First, endo-cardial boundary points are detected by using adaptive thresholding and seeded region growing. To include papillary muscles inside the boundary, the endo-cardial boundary points are refined by using ray-casting based profile. Second, epi-cardial boundary points which have both a myocardial intensity value and a maximum gradient are detected by using ray-casting based adaptive gradient profile. Finally, to preserve an elliptical or circular shape, the endo- and epi-cardial boundary points are refined by using elliptical interpolation and B-spline curve fitting. Then, curvature-based contour fitting is performed to overcome problems associated with heterogeneity of the myocardium intensity and lack of clear delineation between myocardium and adjacent anatomic structures. To evaluate our method, we performed visual inspection, accuracy and processing time. For accuracy evaluation, average distance difference and overalpping region ratio between automatic segmentation and manual segmentation are calculated. Experimental results show that the average distnace difference was $0.56{\pm}0.24mm$. The overlapping region ratio was $82{\pm}4.2%$ on average. In all experimental datasets, the whole process of our method was finished within 1 second.
Keywords
left ventricle; endo- and epicardial contour; Computed Tomography; Automatic Segmentation; ray-casting;
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1 M. Unser, "Splines - a perfect fit for signal/image processing," IEEE Signal Processing Magazine, vol.16, no.6, pp.22-38, 1999.   DOI   ScienceOn
2 H.C. van Assen, M.G. Danilouchkine, et al,, "Cardiac LV segmentation using a 3D active shape model driven by fuzzy inference," in Proc. Medical Image Computing and Computer-Assisted Intervention, vol.2878, pp.533-540, 2003.
3 Fritz D., Kroll J.,, Dillmann R., Scheuering M. "Automatic 4D-Segmentation of the Left Vetnricle in Cardiac-CT-Data," in Proc. International Society for Optical Engineering SPIE, vol.6512, Mar 2007.
4 Muller-Karger C., Wong S., Cruz A., et al., "A Level-set Segmentation Approach for 4-D Cardiac Images," IFMBE proceedings, vol.18, pp.286-289, 2007.
5 M. Lee, H. Hong, "Epi- and Endocardial Segmentation of the Left Ventricle using Adaptive Gradient Profile in CT Cardiac Dataset," Proc. of the 35th KIISE Spring Conference, vol.35, pp. 291-292, 2008. (in Korean)
6 Liang J., Gangyi D., Yuwei W., "Segmentation of the Left Ventricle from Cardiac MR Images Based on Radial GVF Snake," 2008 International Conference on BioMedical Engineering and Informatics, vol.2, pp.238-242, 2008.
7 Robert M. Lapp, Lorenzo-Valdes M., Rueckert D., "3D/4D Cardiac Segmentation Using Active Appearance Models, Non-rigid Registration, and the Insight Toolkit," in Proc. Medical Image Computing and Computer-Assisted Intervention, vol.3216, pp.419-426, 2004.
8 H.C. Assen, R.J. Geest, M.G. Danilouchkine, et al., "3D Active shape Model Matching for Left Ventricle Segmentation in Cardiac CT," Proc. of SPIE, vol.5032, pp.384-393, 2003.
9 Neubaure A., Wegenkittl R., "Skeleton-Based Myocardium Segmentation," Proc. of SPIE, vol.5009 pp.56-67, 2003.
10 Neubaure A., Wegenkittl R., "Analysis of Four- Dimensional Cardiac Data Sets using Skeleton- Based Segmentation," Journal of WSCG, vol.11, no.1, pp.1213-6972, 2003.
11 Mikhail G., Faiza A., Rob J., Boudewijn P., Johan H., "Automated Quantification of Cardiac Short- Axis Multi-Slice CT Images for Assessment of Left Ventricular Global Function," Proc. of SPIE, vol.5747, pp.463-474, 2005.
12 Marie-Pierre Jolly, "Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images," International Journal of Computer vision, vol.70, no.2, pp.151-163, 2006.   DOI   ScienceOn
13 Schlosser T., Pagonidis K., Herborn CU, Hunold P. et al., "Assessment of left ventricular parameters using 16-MDCT and new software for endocardial and epicardial border delineation," An J Roentgenol, vol.184 pp.765-773.
14 Juergens KU, Fischbach R., "Left ventricluar function studied with MDCT," Eur Radiology, vol.16, pp.342-35, 2006.   DOI   ScienceOn
15 G. Mujlenbruch, M. Das, C. Hohl, J. Wildberger et al., "Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm," Eur Radiology, vol.16, pp.1117-1123 2006.   DOI   ScienceOn
16 J. Fleureau, M. Garreau, D. Boulmier, A. Hernandez, "A new 3D Multi-Object Segmentation Method in Cardiac MSCT Imaging using a Multi- Agent Approach," CLAIB2007, IFMBE Proceedings, vol.18, pp.249-253, 2007.
17 Urschler M., Mayer H., Bolter R., Leberl F., "The LiveWire Approach for the Segmentation of Left Ventricle Electron-Beam CT Images," Proc. of 26th Workshop of the Austrian Association for Pattern Recognition: Vision with Non-traditional Sensors, vol.160, pp.319-326, 2002.