L. Ruskó, G. Bekes, G. Németh and M. Fidrich, “Fully Automatic Liver Segmentation for Contrast Enhanced CT Images,” MICCAI Wshp. 3D Segmentation in the Clinic: A Grand Challenge, 2007, pp. 143-150.
 C. M. Li, C. Y. Xu, C. F. Gui and M. D. Fox, “Distance Regularized Level Set Evolution and Its Application to Image Segmentation,” IEEE Transactions onImage Processing, Vol. 19, No. 12, 2010, pp. 3243-3254.
 B. N. Li, C. K. Chui, S. Chang and S. H. Ong, “Integrating Spatial Fuzzy Clustering with Level Set Methods for Automated Medical Image Segmentation,” Computers in Biology and Medicine, Vol. 41, No. 1, 2011, pp. 1–10. doi:10.1016/j.compbiomed.2010.10.007
 C. Platero, M. C. Tobar, J. Sanguino, J. M. Poncela and O. Velasco, “Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors,” Pattern Recognition and Image Analysis, Vol. 6669,2011, pp. 109-116. doi:10.1007/978-3-642-21257-4_14
 Y. Q. Zhao, Y. L. Zan, X. F. Wang and G. Y. Li, “Fuzzy C-means Clustering-Based Multilayer Perception Neural Network for Liver CT Images Automatic Segmentation,” Control and Decision Conference (CCDC), Xuzhou, May 2010, pp. 3423-3427.
 K. S. Chuang, H. L. T. zeng, S. Chen, J. Wu and J. Chen, “Fuzzy C-means Image Segmentation with Weighted Membership Functions with Spatial Constraints,” Computerized Medical Imaging and Graphics, Vol. 30, No. 1,2006, pp. 9–15. doi:10.1016/j.compmedimag.2005.10.001
 X. Zhang, J. Tian, K. X. Deng, Y. F. Wu and X. L. Li: “Automatic Liver Segmentation Using a Statistical Shape Model with Optimal Surface Detection,” IEEE Transactions on Biomedical Engineering, Vol. 57, No. 10, 2010, pp. 2622-2626. doi.org/10.1109/TBME.2010.2056369
 S. Luo, Q. Hu, X. He, J. Li, J. Jin and M. Park, “Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines,” Proceedings of 2009 Icme International Conference on Complex Medical Engineering, Tempe, 9-11 April 2009, pp. 1-5.
 X. Zhang, J. Tian, D. H. Xiang, X. L. Li and K. X. Deng, “Interactive liver tumor segmentation from CT scans using support vector classification with watershed,” Engineering in Medicine and Biology Society, EMBC, 2011, pp. 6005-6008.