Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning


The structure and morphology of the hepatic vessels and their relationship between tumors and liver segments are major interests to surgeons for liver surgical planning. In case of living donor liver transplantation (LDLT), the most important step in determining donor suitability is an accurate assessment of the liver volume available for transplantation. In addition, the mutual principles of the procedures include dissection in the appropriate anatomic plane without portal occlusion, minimization of blood loss, and avoidance of injury to the remaining liver. It is essential first step to identify and evaluate the major hepatic vascular structure for liver surgical planning. In this paper, the threshold was determined to segment the liver region automatically based on the distribution ratio of intensity value; and the hepatic vessels were extracted with mathematical morphology transformation, which called hit operation, that is slightly modified version of hit-and-miss operation on contrast enhanced CT image sequence. We identified the vein using the preserved voxel connectivity between two consecutive transverse image sequences, followed by resection into right lobe including right hepatic vein, middle hepatic vein branches andleft lobe including left hepatic vein. An automated hepatic vessel segmentation scheme is recommended for liver surgical planning such as tumor resection and transplantation. These vessel extraction method combined with liver region segmentation technique could be applicable to extract tree-like organ structures such as carotid, renal, coronary artery, and airway path from various medical imaging modalities.

Share and Cite:

Kim, D. (2013) Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning. Journal of Biomedical Science and Engineering, 6, 498-503. doi: 10.4236/jbise.2013.64063.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Heneghan, M.A. and O’Grady, J.G. (1999) Liver transplantation for malignant disease. Best Practice & Research Clinical Gastroenterology, 13, 575-591. doi:10.1053/bega.1999.0049
[2] Orloff, M., Bozorgzadeh, A., Lansing, K., Cullen, J., Ryan, C.K., Jain, A., et al. (2004) Post-operative liver dysfunction following donation of segmental liver grafts. American Journal of Transplantation, 4, 532.
[3] Pham, D.L., Xu, C. and Prince, J.L. (2000) Current method in medical image segmentation. Annual Review Biomedical Engineering, 2, 315-317. doi:10.1146/annurev.bioeng.2.1.315
[4] Haralick, R.M. and Shapiro, L.G. (1985) Image segmentation technique. Computer Vision Graphics and Image Processing, 29, 100-132. doi:10.1016/S0734-189X(85)90153-7
[5] Klingler, J.W., Vaughan, C.L., Fraker, T.D. and Andrews, L.T. (1988) Segmentation of echocardiographic images using mathematical morphology. IEEE Transactions on Biomedical Engineering, 35, 925-934. doi:10.1109/10.8672
[6] Kim, D.Y. and Park, J.W. (2005) Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images. Image and Vision Computing, 23, 1277-1287. doi:10.1016/j.imavis.2005.09.005
[7] Haralick, R.M., Stenberg, S.R. and Zhuang, X. (1987) Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, 532-550. doi:10.1109/TPAMI.1987.4767941
[8] Adams, R. and Bischof, L. (1994) Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 641-647. doi:10.1109/34.295913
[9] Mehnert, A. and Jackway, P. (1997) An improved seeded region growing algorithm. Pattern Recognition Letter, 18, 1065-1071. doi:10.1016/S0167-8655(97)00131-1
[10] Wang, S.Y. and Higgins, W.E. (2003) Symmetric region growing. IEEE Transactions on Image Processing, 12, 1007-1015. doi:10.1109/TIP.2003.815258
[11] Chang, F., Chen, C.J. and Lu, C.J. (2004) A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding, 93, 206-220. doi:10.1016/j.cviu.2003.09.002
[12] Hu, Q., Qian, G. and Nowinski, W.L. (2005) Fast connected-component labeling in three-dimensional binary images based on iterative recursion. Computer Vision and Image Understanding, 99, 414-434. doi:10.1016/j.cviu.2005.04.001
[13] Gao, L., Heath, D.G., Kuszyk, B.S. and Fishman, E.K. (1996) Automatic liver segmentation technique for three- dimensional visualization of CT data. Radiology, 201, 359-364.
[14] Gao, L., Heath, D.G. and Fishman, E.K. (1998) Abdominal image segmentation using three-dimensional deformable models. Investigative Radiology, 33, 348-355. doi:10.1097/00004424-199806000-00006
[15] Chen, E.L., Chung, P.C., Chen, C.L., Tsai, H.M. and Chang, C.I. (1998) An automatic diagnostic system for CT liver image classification. IEEE Transactions on Biomedical Engineering, 45, 783-794. doi:10.1109/10.678613
[16] Farjo, L.A., Williams, D.M., Bland, P.H., Francis, I.R. and Meyer, C.R. (1992) Determination of liver volume from CT scans using histogram cluster analysis. Journal of Computer Assisted Tomography, 16, 674-683. doi:10.1097/00004728-199209000-00002
[17] Bae, K.T., Giger, M.L., Chen, C.T. and Kahn, C.E. (1993) Automatic segmentation of liver structure in CT images. Medical Physics, 20, 71-78. doi:10.1118/1.597064
[18] Levoy, M. (1988) Display of surfaces from volume data. IEEE Computer Graphics and Applications, 8, 29-37. doi:10.1109/38.511
[19] Kirbas, C. and Quek, F. (2004) A review of vessel extraction techniques and algorithms. ACM Computing Surveys, 36, 81-121. doi:10.1145/1031120.1031121
[20] Kim, D.Y. and Park, J.W. (2009) Multiple-phase segmentation approach for blood vessel extraction on cervical MRA image sequence. Magnetic Resonance Imaging, 27, 256-263. doi:10.1016/j.mri.2008.06.012

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.