S. Priyadarsini and D. Selvathi, “Survey on Segmentation of Liver from CT Images,” 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies, 2012.
 Mharib, A.M., et al., “Survey on liver CT image segmentation methods,” Artificial Intelligence Review, Vol. 37, No. 2, 2012, pp. 38-95.
 S. Rathore, et al., “Texture Analysis for Liver Segmentation and Classification: A Survey,” Frontiers of Information Technology, 2011, pp. 121-126.
 P. Campadelli, E. Casiraghi and A. Esposito, “Liver Segmentation from Computed Tomography Scans: A Survey and a New Algorithm,” Artificial Intelligence in Medicine, Vol. 45, No. 2-3, 2009, pp. 185-196.
 R. Punia and S. Singh, “Review on Machine Learning Techniques for Automatic Segmentation of Liver Images,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 4, 2013, pp. 666-670.
 R. Adams and L. Bischof, “Seeded Region Growing,” IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 16, 1994, pp. 641-647.
 J. Y. Zhou, et al., “Liver Tumour Segmentation Using Contrast-Enhanced Multi-Detector CT Data: Performance Benchmarking of Three Semiautomated Methods,” European Radiology, Vol. 20, No. 7, 2010, pp. 1738-1748.
 R. Pohle and K. D. Toennies, “Segmentation of Medical Images Using Adaptive Region Growing,” Proceedings of SPIE Medical Imaging, Vol. 43, No. 22, 2001, pp. 1337-1346.
 K. J. Mortele, V. Cantisani and R. Troisi, “Preoperative Liver Donor Evaluation: Imaging and Pitfalls,” Liver Transplantation, Vol. 9, No. 9, 2003, pp. 6-14.
 L. Ruskó, et al., “Fully Automatic Liver Segmentation for Contrast-Enhanced CT Images,” MICCAI Wshp. 3D Segmentation in the Clinic: A Grand Challenge, 2007.
 S. S. Kumar, R. S. Moni and J. Rajeesh, “Automatic Liver and Lesion Segmentation: A Primary Step in Diagnosis of Liver Diseases,” Signal, Image and Video Processing, Vol. 7, No. 1, 2011, pp. 163-172.
 J. Huang, et al., “Based on Statistical Analysis and 3D Region Growing Segmentation Method of Liver,” Advanced Computer Control (ICACC), 2011, pp. 478-489.
 C. Xu, A. Y. Jr. and J. L. Prince, “On the Relationship between Parametric and Geometric Active Contours,” 34th Asilomar Conference on Signals, Systems, and Computers, 2000.
 C. Xu and J. L. Prince, “Snakes, Shapes, and Gradient Vector Flow,” IEEE Transactions on Image Process, Vol. 7, 1998, pp. 359-369.
 Y. Shang, et al., “Vascular Active Contour for Vessel Tree Segmentation,” IEEE Transactions on Biomedical Engineering, Vol. 58, No. 4, 2011, pp. 1023-1032.
 M. G. Linguraru, et al., “Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor Segmentation,” IEEE Transactions on Medical Imaging, Vol. 31, No. 10, 2012, pp. 1965-1976.
 C. Latero, et al., “Liver segmentation for hepatic lesions detection and characterization,” Biomedical Imaging: From Nano to Macro, 2008.
 K. Suzuki, et al., “Computer-Aided Measurement of Liver Volumes in CT by Means of Geodesic Active Contour Segmentation Coupled with Level-Set Algorithms,” Medical Physics, Vol. 37, No. 5, 2010, p. 2159.
 Li, B.N., et al., “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.
 D. A. Oliveira, R. Q. Feitosa and M. M. Correia, “Liver Segmentation using Level Sets and Genetic Algorithms,” Fourth International Conference on Computer Vision Theory and Applications, Porto, 2009.
 D. A. Oliveira, R. Q. Feitosa and M. M. Correia, “Segmentation of Liver, Its Vessels and Lesions from CT Images for Surgical Planning,” Biomed Eng Online, Vol. 10, 2011, p. 30. http://dx.doi.org/10.1186/1475-925X-10-30
 C. Platero, et al., “Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors,” Pattern Recognition and Image Analysis, 2011, pp. 109-116.
 D. Jimenez-Carretero, et al., “Optimal Multiresolution 3D Level-Set Method for Liver Segmentation Incorporating Local Curvature Constraints,” Engineering in Medicine and Biology Society, 2011, pp. 3419-3422.
 H. Yang, et al., “A Novel Graph Cuts Based Liver Segmentation Method,” Medical Image Analysis and Clinical Applications, 2010, pp. 50-53.
 L. Massoptier and S. Casciaro, “Fully Automatic Liver Segmentation through Graph-Cut Technique,” Engineering in Medicine and Biology Society, 2007, pp. 5243- 5246.
 Y. W. Chen, K. Tsubokawa and A. H. Foruzan, “Liver Segmentation from Low Contrast Open MR Scans Using K-Means Clustering and Graph-Cuts,” Proceedings of Advances In Neural Networks-Isnn 2010, Pt 2, 2010.
 N. H. Abdel-massieh, M. M. Hadhoud and K. M. Amin, “Fully Automatic Liver Tumor Segmentation from Abdominal CT Scans,” Computer Engineering and Systems, 2010, pp. 197-202.
 N. H. Abdel-massieh, M. M. Hadhoud and K. M. Amin, “Automatic Liver Tumor Segmentation from CT Scans with Knowledge-Based Constraints,” Biomedical Engineering Conference, 2010.
 D. Lu, et al., “A Fast and Robust Approach to Liver Nodule Detection in MR Images,” Frontiers in the Convergence of Bioscience and Information Technologies, 2007.
 Y. Zhao, et al., “Fuzzy C-Means Clustering-Based Multilayer Perceptron Neural Network for Liver CT Images Automatic Segmentation,” Control and Decision Conference, 2010.
 S. S. Kumar, R. S. Moni and J. Rajeesh, “Liver Tumor Diagnosis by Gray Level and Contourlet Coefficients Texture Analysis,” Computing, Electronics and Electrical Technologies, 2012, pp. 557-562.
 Z. Yuan, et al., “A Novel Automatic Liver Segmentation Technique for MR Images,” 3rd International Congress on Image and Signal Processing, 2010.
 Y.-W. Chen, et al., “Image Segmentation and Registration Techniques for MR-Guided Liver Cancer Surgery,” Mechatronics and Embedded Systems and Applications, 2012, pp. 105-108.
 A. H. Foruzan, et al., “Segmentation of Liver in Low-Contrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms,” IEICE Transactions on Information and Systems, Vol. 96, No. 4, 2013, pp. 798-807.
 D. Chi, Y. Zhao and M. Li, “Automatic Liver MR Image Segmentation with Self-Organizing Map and Hierarchical Agglomerative Clustering Method,” Image and Signal Processing, 2010, pp. 1333-1337.
 Y. Masuda, et al., “Automatic Liver Tumor Detection Using EM/MPM Algorithm and Shape Information,” Software Engineering and Data Mining, 2010, pp. 692- 695.
 J. Lu, et al., “An Interactive Approach to Liver Segmentation in CT Based on Deformable Model Integrated with Attractor Force,” Machine Learning and Cybernetics, 2011, pp. 1660-1665.
 F. Jia, C. Huang, Q. Hu, C. Fang and Y. Fan, “Automatic Liver Detection and Segmentation from 3D CT Images: A Hybrid Method Using Statistical Pose Model and Proba-bilistic Atlas,” International Journal of Computer Assited Radiology and Surgery, Vol. 8, No. 1, 2013, pp. 237-239.
 X. Zhang, et al., “Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection,” IEEE Transaction on Biomedical Engineering, Vol. 57, No. 10, 2010, pp. 2611-2626.
 M. Erdt, et al., “Fast Automatic Liver Segmentation Combining Learned Shape Priors with Observed Shape Deviation,” Computer-Based Medical Systems, 2010, pp. 249-254.
 H. Badakhshannoory and P. Saeedi, “A Model-Based Validation Scheme for Organ Segmentation in CT Scan Volumes,” IEEE Transaction on Biomedical Engineering, 2009, pp. 2681-2693.
 W. Huang, et al., “A Semi-Automatic Approach to the Segmentation of Liver Parenchyma from 3D CT Images with Extreme Learning Machine,” 34th Annual International Conference of the IEEE EMBS, 2012.
 H. Ji, et al., “ACM-Based Automatic Liver Segmentation from 3D CT Images by Combining Multiple Atlases and Improved Mean Shift Techniques,” IEEE Transactions on Information Technology in Biomedicine, 2013, pp. 1-9.
 M. Danciu, et al., “3D DCT Supervised Segmentation Applied on Liver Volumes,” Telecommunications and Signal Processing, 2012, pp. 779-783.
 S. Luo, et al., “Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines,” Proceedings of 2009 ICME International Conference on Complex Medical Engineering, 2009.
 S. Luo, X. Li and J. Li, “Improvement of Liver Segmentation by Combining High Order Statistical Texture Features with Anatomical Structural Features,” Journal of Signal and Information Processing, 2013, pp. 67-72.
 X. Zhang, et al., “Interactive Liver Tumor Segmentation from CT Scans Using Support Vector Classification with Watershed,” Engineering in Medicine and Biology Society, 2011, pp. 6005-6008.
 A. Rafiee, H. Masoumi and A. Roosta, “Using neural network for liver detection in abdominal MRI images,” Signal and Image Processing Applications, 2009, pp. 21- 26.
 M. Sammouda, et al., “Tissue Color Images Segmentation Using Artificial Neural Networks,” Biomedical Imaging: Nano to Macro, 2004.