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.
Cite this paper
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.
 Heneghan, M.A. and O’Grady, J.G. (1999) Liver transplantation for malignant disease. Best Practice & Research Clinical Gastroenterology, 13, 575-591.
 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.
 Pham, D.L., Xu, C. and Prince, J.L. (2000) Current method in medical image segmentation. Annual Review Biomedical Engineering, 2, 315-317.
 Haralick, R.M. and Shapiro, L.G. (1985) Image segmentation technique. Computer Vision Graphics and Image Processing, 29, 100-132.
 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.
 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
 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
 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
 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
 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
 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
 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.
 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.
 Gao, L., Heath, D.G. and Fishman, E.K. (1998) Abdominal image segmentation using three-dimensional deformable models. Investigative Radiology, 33, 348-355.
 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.
 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.
 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
 Levoy, M. (1988) Display of surfaces from volume data. IEEE Computer Graphics and Applications, 8, 29-37.
 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
 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