Liver Segmentation and 3D Modeling Based on Multilayer Spiral CT Image

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)


The 3D reconstruction can facilitate the diagnosis of liver disease by making the target easier to identify and revealing the volume and shape much better than 2D imaging. In this paper, in order to realize 3D reconstruction of liver parenchyma, a series of pretreatments are carried out, including windowing conversion, filtering and liver parenchyma extraction. Furthermore, three kinds of modeling methods were researched to reconstruct the liver parenchyma containing surface rending, volume rendering and point rendering. The MC (marching cubes) algorithm based on 3D region growth is proposed to overcome the existence of a large number of voids and long modeling time for the contours of traditional MC algorithms. Simulation results of the three modeling methods show different advantages and disadvantages. The surface rendering can intuitively image on the liver surface modeling, but it cannot reflect the inside information of the liver. The volume rendering can reflect the internal information of the liver, but it requires a higher computer performance. The point rendering modeling speed is quickly compared to the surface rendering and the volume rendering, whereas the modeling effect is rough. Therefore, we can draw a conclusion that different modeling methods should be selected for different requirements.


Liver CT image Liver segmentation 3D modeling MC algorithm 



This research is supported by Jilin Province Nature Science Foundation (No. 20130102082JC), Jilin Province Development and Innovation Committee’s High and New Technology Projects (No. JF2012C006-6).


  1. 1.
    Chen, L., Luo, H., Dong, S., et al.: Safety assessment of hepatectomy for huge hepatocellular carcinoma by three dimensional reconstruction technique. Chin. J. Surg. 54(9), 669–674 (2016)Google Scholar
  2. 2.
    Huang, Q.: Research of 3D modeling and liver volume calculation method based on liver CT image. Jilin University (2016)Google Scholar
  3. 3.
    Fang, C., Feng, S., Fan, Y., et al.: Study on the application of three-dimensional visualization technique in evaluation of residual liver volume and guidance for hepatectomy. J. Hepatobiliary Surg. 20(2), 96–98 (2012)Google Scholar
  4. 4.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comput. Graph. 21(4), 163–169 (1987)CrossRefGoogle Scholar
  5. 5.
    Nielson, G.M., Hamann, B.: The asymptotic decider: resolving the ambiguity in marching cubes. In: Proceedings of the 2nd Conference on Visualization, pp. 83–91. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  6. 6.
    Zhou, J.: 3D-volume reconstruction of medical images based on ray-casting algorithm. Comput. Sci. 43(11A), 156–160 (2016)Google Scholar
  7. 7.
    Hu, Z., Gui, J., Zhou, Y., et al.: Methods for implementation of CT visualization based on surface and volume rendering. Microcomput. Inf. 25(12), 107–108 (2009)Google Scholar
  8. 8.
    Liu, L.: Research of liver image segmentation method and 3D modeling method based on multilayer spiral CT. Jilin University (2015)Google Scholar
  9. 9.
    Fu, L., Yao, Y., Fu, Z.: Filtering method for medical images based on median filtering and anisotropic diffusion. J. Comput. Appl. 34(1), 145–148 (2014)Google Scholar
  10. 10.
    Sun, Y., Liu, L., Huang, Q., et al.: Liver image segmentation algorithm based on RBF confidence interval. J. Inf. Comput. Sci. 12(5), 1703–1711 (2015)CrossRefGoogle Scholar
  11. 11.
    Sun, J., Wu, B., Liu, X.: Cellular neural network applicating manner in pre processing image. Chin. J. Comput. 28(6), 985–990 (2005)Google Scholar
  12. 12.
    Wang, M., Feng, J., Yang, B.: Comparsion and evaluation of marching cubes and marching tetrahedra. J. Comput.-Aided Des. Comput. Graph. 26(12), 2099–2106 (2014)Google Scholar
  13. 13.
    Li, L.: Design method of transfer function for 3D medical data volume rendering. University of Science and Technology of China (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of SoftwareJilin UniversityChangchunChina
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchunChina

Personalised recommendations